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  1. Article: Prevalence of Sinus Mucosal Abnormalities on CT of the Head Performed for Headache When Compared With Those Performed for Other Indications.

    Kalidindi, Sadhana / Gandhi, Sanjay

    Cureus

    2024  Volume 16, Issue 3, Page(s) e56608

    Abstract: Background There is a high prevalence of mucosal abnormalities of paranasal sinuses on CT Head scans performed for all indications. The purpose of this study is to see whether or not such abnormalities are more common in scans performed on patients ... ...

    Abstract Background There is a high prevalence of mucosal abnormalities of paranasal sinuses on CT Head scans performed for all indications. The purpose of this study is to see whether or not such abnormalities are more common in scans performed on patients presenting with headaches when compared with those without headaches. Methods Images of CT scans of the brain of 100 consecutive patients from each of the two study groups (a total of 200 scans) were retrospectively reviewed for the presence of sinus mucosal abnormalities and their Lund-Mackay (LM) scores were calculated. A corrected LM score was also calculated using a correction factor for non-visualized sinuses in some scans and osteomeatal complexes in all scans. Radiological reports for these scans were also reviewed to note whether or not they contained any comments on the sinuses. All the reviewed scans were performed between January 1, 2021 and January 22, 2021. Results In the headache group, 17 patients had an LM score above 4 (which was used as the main cut-off point for this study). In the non-headache group, 16 patients had a score greater than 4. The mean LM score in the headache group was 1.24 and in the non-headache group was 1.4. There has been no significant difference in the comparison when corrected LM scores were used. In the headache group, 22 radiology reports contained comments on the sinuses compared to 11 reports in the non-headache group. Conclusion Results of this study indicate that there is no significant difference in the prevalence of clinically important sinus mucosal abnormalities in patients who had a brain CT for headache when compared with other indications. It was found that radiologists tend to comment on the sinuses more often when the indication was headache. It may be reasonable for radiologists to consider reviewing this practice. This might reduce unnecessary referrals to ENT and, more importantly, avoid missing other reasons for headaches.
    Language English
    Publishing date 2024-03-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2747273-5
    ISSN 2168-8184
    ISSN 2168-8184
    DOI 10.7759/cureus.56608
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Workforce Crisis in Radiology in the UK and the Strategies to Deal With It: Is Artificial Intelligence the Saviour?

    Kalidindi, Sadhana / Gandhi, Sanjay

    Cureus

    2023  Volume 15, Issue 8, Page(s) e43866

    Abstract: Radiology has seen rapid growth over the last few decades. Technological advances in equipment and computing have resulted in an explosion of new modalities and applications. However, this rapid expansion of capability and capacity has not been matched ... ...

    Abstract Radiology has seen rapid growth over the last few decades. Technological advances in equipment and computing have resulted in an explosion of new modalities and applications. However, this rapid expansion of capability and capacity has not been matched by a parallel growth in the number of radiologists. This has resulted in global shortages in the workforce, with the UK being one of the most affected countries. The UK National Health Service has been employing several conventional strategies to deal with the workforce situation with mixed success. The emergence of artificial intelligence (AI) tools that have the potential to increase efficiency and efficacy at various stages in radiology has made it possible for radiology departments to use new strategies and workflows that can offset workforce shortages to some extent. This review article discusses the current and projected radiology workforce situation in the UK and the various strategies to deal with it, including applications of AI in radiology. We highlight the benefits of AI tools in improving efficiency and patient safety. AI has a role along the patient's entire journey from the clinician requesting the appropriate radiological investigation, safe image acquisition, alerting the radiologists and clinicians about critical and life-threatening situations, cancer screening follow up, to generating meaningful radiology reports more efficiently. It has great potential in easing the workforce crisis and needs rapid adoption by radiology departments.
    Language English
    Publishing date 2023-08-21
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2747273-5
    ISSN 2168-8184
    ISSN 2168-8184
    DOI 10.7759/cureus.43866
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Hotline: A Poem.

    Kalidindi, Sadhana

    Annals of internal medicine

    2019  Volume 170, Issue 4, Page(s) 273

    Language English
    Publishing date 2019-02-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 336-0
    ISSN 1539-3704 ; 0003-4819
    ISSN (online) 1539-3704
    ISSN 0003-4819
    DOI 10.7326/M18-2649
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Exploring UK medical school differences: the MedDifs study of selection, teaching, student and F1 perceptions, postgraduate outcomes and fitness to practise.

    McManus, I C / Harborne, Andrew Christopher / Horsfall, Hugo Layard / Joseph, Tobin / Smith, Daniel T / Marshall-Andon, Tess / Samuels, Ryan / Kearsley, Joshua William / Abbas, Nadine / Baig, Hassan / Beecham, Joseph / Benons, Natasha / Caird, Charlie / Clark, Ryan / Cope, Thomas / Coultas, James / Debenham, Luke / Douglas, Sarah / Eldridge, Jack /
    Hughes-Gooding, Thomas / Jakubowska, Agnieszka / Jones, Oliver / Lancaster, Eve / MacMillan, Calum / McAllister, Ross / Merzougui, Wassim / Phillips, Ben / Phillips, Simon / Risk, Omar / Sage, Adam / Sooltangos, Aisha / Spencer, Robert / Tajbakhsh, Roxanne / Adesalu, Oluseyi / Aganin, Ivan / Ahmed, Ammar / Aiken, Katherine / Akeredolu, Alimatu-Sadia / Alam, Ibrahim / Ali, Aamna / Anderson, Richard / Ang, Jia Jun / Anis, Fady Sameh / Aojula, Sonam / Arthur, Catherine / Ashby, Alena / Ashraf, Ahmed / Aspinall, Emma / Awad, Mark / Yahaya, Abdul-Muiz Azri / Badhrinarayanan, Shreya / Bandyopadhyay, Soham / Barnes, Sam / Bassey-Duke, Daisy / Boreham, Charlotte / Braine, Rebecca / Brandreth, Joseph / Carrington, Zoe / Cashin, Zoe / Chatterjee, Shaunak / Chawla, Mehar / Chean, Chung Shen / Clements, Chris / Clough, Richard / Coulthurst, Jessica / Curry, Liam / Daniels, Vinnie Christine / Davies, Simon / Davis, Rebecca / De Waal, Hanelie / Desai, Nasreen / Douglas, Hannah / Druce, James / Ejamike, Lady-Namera / Esere, Meron / Eyre, Alex / Fazmin, Ibrahim Talal / Fitzgerald-Smith, Sophia / Ford, Verity / Freeston, Sarah / Garnett, Katherine / General, Whitney / Gilbert, Helen / Gowie, Zein / Grafton-Clarke, Ciaran / Gudka, Keshni / Gumber, Leher / Gupta, Rishi / Harlow, Chris / Harrington, Amy / Heaney, Adele / Ho, Wing Hang Serene / Holloway, Lucy / Hood, Christina / Houghton, Eleanor / Houshangi, Saba / Howard, Emma / Human, Benjamin / Hunter, Harriet / Hussain, Ifrah / Hussain, Sami / Jackson-Taylor, Richard Thomas / Jacob-Ramsdale, Bronwen / Janjuha, Ryan / Jawad, Saleh / Jelani, Muzzamil / Johnston, David / Jones, Mike / Kalidindi, Sadhana / Kalsi, Savraj / Kalyanasundaram, Asanish / Kane, Anna / Kaur, Sahaj / Al-Othman, Othman Khaled / Khan, Qaisar / Khullar, Sajan / Kirkland, Priscilla / Lawrence-Smith, Hannah / Leeson, Charlotte / Lenaerts, Julius Elisabeth Richard / Long, Kerry / Lubbock, Simon / Burrell, Jamie Mac Donald / Maguire, Rachel / Mahendran, Praveen / Majeed, Saad / Malhotra, Prabhjot Singh / Mandagere, Vinay / Mantelakis, Angelos / McGovern, Sophie / Mosuro, Anjola / Moxley, Adam / Mustoe, Sophie / Myers, Sam / Nadeem, Kiran / Nasseri, Reza / Newman, Tom / Nzewi, Richard / Ogborne, Rosalie / Omatseye, Joyce / Paddock, Sophie / Parkin, James / Patel, Mohit / Pawar, Sohini / Pearce, Stuart / Penrice, Samuel / Purdy, Julian / Ramjan, Raisa / Randhawa, Ratan / Rasul, Usman / Raymond-Taggert, Elliot / Razey, Rebecca / Razzaghi, Carmel / Reel, Eimear / Revell, Elliot John / Rigbye, Joanna / Rotimi, Oloruntobi / Said, Abdelrahman / Sanders, Emma / Sangal, Pranoy / Grandal, Nora Sangvik / Shah, Aadam / Shah, Rahul Atul / Shotton, Oliver / Sims, Daniel / Smart, Katie / Smith, Martha Amy / Smith, Nick / Sopian, Aninditya Salma / South, Matthew / Speller, Jessica / Syer, Tom J / Ta, Ngan Hong / Tadross, Daniel / Thompson, Benjamin / Trevett, Jess / Tyler, Matthew / Ullah, Roshan / Utukuri, Mrudula / Vadera, Shree / Van Den Tooren, Harriet / Venturini, Sara / Vijayakumar, Aradhya / Vine, Melanie / Wellbelove, Zoe / Wittner, Liora / Yong, Geoffrey Hong Kiat / Ziyada, Farris / Devine, Oliver Patrick

    BMC medicine

    2020  Volume 18, Issue 1, Page(s) 136

    Abstract: Background: Medical schools differ, particularly in their teaching, but it is unclear whether such differences matter, although influential claims are often made. The Medical School Differences (MedDifs) study brings together a wide range of measures of ...

    Abstract Background: Medical schools differ, particularly in their teaching, but it is unclear whether such differences matter, although influential claims are often made. The Medical School Differences (MedDifs) study brings together a wide range of measures of UK medical schools, including postgraduate performance, fitness to practise issues, specialty choice, preparedness, satisfaction, teaching styles, entry criteria and institutional factors.
    Method: Aggregated data were collected for 50 measures across 29 UK medical schools. Data include institutional history (e.g. rate of production of hospital and GP specialists in the past), curricular influences (e.g. PBL schools, spend per student, staff-student ratio), selection measures (e.g. entry grades), teaching and assessment (e.g. traditional vs PBL, specialty teaching, self-regulated learning), student satisfaction, Foundation selection scores, Foundation satisfaction, postgraduate examination performance and fitness to practise (postgraduate progression, GMC sanctions). Six specialties (General Practice, Psychiatry, Anaesthetics, Obstetrics and Gynaecology, Internal Medicine, Surgery) were examined in more detail.
    Results: Medical school differences are stable across time (median alpha = 0.835). The 50 measures were highly correlated, 395 (32.2%) of 1225 correlations being significant with p < 0.05, and 201 (16.4%) reached a Tukey-adjusted criterion of p < 0.0025. Problem-based learning (PBL) schools differ on many measures, including lower performance on postgraduate assessments. While these are in part explained by lower entry grades, a surprising finding is that schools such as PBL schools which reported greater student satisfaction with feedback also showed lower performance at postgraduate examinations. More medical school teaching of psychiatry, surgery and anaesthetics did not result in more specialist trainees. Schools that taught more general practice did have more graduates entering GP training, but those graduates performed less well in MRCGP examinations, the negative correlation resulting from numbers of GP trainees and exam outcomes being affected both by non-traditional teaching and by greater historical production of GPs. Postgraduate exam outcomes were also higher in schools with more self-regulated learning, but lower in larger medical schools. A path model for 29 measures found a complex causal nexus, most measures causing or being caused by other measures. Postgraduate exam performance was influenced by earlier attainment, at entry to Foundation and entry to medical school (the so-called academic backbone), and by self-regulated learning. Foundation measures of satisfaction, including preparedness, had no subsequent influence on outcomes. Fitness to practise issues were more frequent in schools producing more male graduates and more GPs.
    Conclusions: Medical schools differ in large numbers of ways that are causally interconnected. Differences between schools in postgraduate examination performance, training problems and GMC sanctions have important implications for the quality of patient care and patient safety.
    MeSH term(s) Female ; Humans ; Male ; Schools, Medical/standards ; Students, Medical/statistics & numerical data ; United Kingdom
    Language English
    Publishing date 2020-05-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2131669-7
    ISSN 1741-7015 ; 1741-7015
    ISSN (online) 1741-7015
    ISSN 1741-7015
    DOI 10.1186/s12916-020-01572-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: The Analysis of Teaching of Medical Schools (AToMS) survey: an analysis of 47,258 timetabled teaching events in 25 UK medical schools relating to timing, duration, teaching formats, teaching content, and problem-based learning.

    Devine, Oliver Patrick / Harborne, Andrew Christopher / Horsfall, Hugo Layard / Joseph, Tobin / Marshall-Andon, Tess / Samuels, Ryan / Kearsley, Joshua William / Abbas, Nadine / Baig, Hassan / Beecham, Joseph / Benons, Natasha / Caird, Charlie / Clark, Ryan / Cope, Thomas / Coultas, James / Debenham, Luke / Douglas, Sarah / Eldridge, Jack / Hughes-Gooding, Thomas /
    Jakubowska, Agnieszka / Jones, Oliver / Lancaster, Eve / MacMillan, Calum / McAllister, Ross / Merzougui, Wassim / Phillips, Ben / Phillips, Simon / Risk, Omar / Sage, Adam / Sooltangos, Aisha / Spencer, Robert / Tajbakhsh, Roxanne / Adesalu, Oluseyi / Aganin, Ivan / Ahmed, Ammar / Aiken, Katherine / Akeredolu, Alimatu-Sadia / Alam, Ibrahim / Ali, Aamna / Anderson, Richard / Ang, Jia Jun / Anis, Fady Sameh / Aojula, Sonam / Arthur, Catherine / Ashby, Alena / Ashraf, Ahmed / Aspinall, Emma / Awad, Mark / Yahaya, Abdul-Muiz Azri / Badhrinarayanan, Shreya / Bandyopadhyay, Soham / Barnes, Sam / Bassey-Duke, Daisy / Boreham, Charlotte / Braine, Rebecca / Brandreth, Joseph / Carrington, Zoe / Cashin, Zoe / Chatterjee, Shaunak / Chawla, Mehar / Chean, Chung Shen / Clements, Chris / Clough, Richard / Coulthurst, Jessica / Curry, Liam / Daniels, Vinnie Christine / Davies, Simon / Davis, Rebecca / De Waal, Hanelie / Desai, Nasreen / Douglas, Hannah / Druce, James / Ejamike, Lady-Namera / Esere, Meron / Eyre, Alex / Fazmin, Ibrahim Talal / Fitzgerald-Smith, Sophia / Ford, Verity / Freeston, Sarah / Garnett, Katherine / General, Whitney / Gilbert, Helen / Gowie, Zein / Grafton-Clarke, Ciaran / Gudka, Keshni / Gumber, Leher / Gupta, Rishi / Harlow, Chris / Harrington, Amy / Heaney, Adele / Ho, Wing Hang Serene / Holloway, Lucy / Hood, Christina / Houghton, Eleanor / Houshangi, Saba / Howard, Emma / Human, Benjamin / Hunter, Harriet / Hussain, Ifrah / Hussain, Sami / Jackson-Taylor, Richard Thomas / Jacob-Ramsdale, Bronwen / Janjuha, Ryan / Jawad, Saleh / Jelani, Muzzamil / Johnston, David / Jones, Mike / Kalidindi, Sadhana / Kalsi, Savraj / Kalyanasundaram, Asanish / Kane, Anna / Kaur, Sahaj / Al-Othman, Othman Khaled / Khan, Qaisar / Khullar, Sajan / Kirkland, Priscilla / Lawrence-Smith, Hannah / Leeson, Charlotte / Lenaerts, Julius Elisabeth Richard / Long, Kerry / Lubbock, Simon / Burrell, Jamie Mac Donald / Maguire, Rachel / Mahendran, Praveen / Majeed, Saad / Malhotra, Prabhjot Singh / Mandagere, Vinay / Mantelakis, Angelos / McGovern, Sophie / Mosuro, Anjola / Moxley, Adam / Mustoe, Sophie / Myers, Sam / Nadeem, Kiran / Nasseri, Reza / Newman, Tom / Nzewi, Richard / Ogborne, Rosalie / Omatseye, Joyce / Paddock, Sophie / Parkin, James / Patel, Mohit / Pawar, Sohini / Pearce, Stuart / Penrice, Samuel / Purdy, Julian / Ramjan, Raisa / Randhawa, Ratan / Rasul, Usman / Raymond-Taggert, Elliot / Razey, Rebecca / Razzaghi, Carmel / Reel, Eimear / Revell, Elliot John / Rigbye, Joanna / Rotimi, Oloruntobi / Said, Abdelrahman / Sanders, Emma / Sangal, Pranoy / Grandal, Nora Sangvik / Shah, Aadam / Shah, Rahul Atul / Shotton, Oliver / Sims, Daniel / Smart, Katie / Smith, Martha Amy / Smith, Nick / Sopian, Aninditya Salma / South, Matthew / Speller, Jessica / Syer, Tom J / Ta, Ngan Hong / Tadross, Daniel / Thompson, Benjamin / Trevett, Jess / Tyler, Matthew / Ullah, Roshan / Utukuri, Mrudula / Vadera, Shree / Van Den Tooren, Harriet / Venturini, Sara / Vijayakumar, Aradhya / Vine, Melanie / Wellbelove, Zoe / Wittner, Liora / Yong, Geoffrey Hong Kiat / Ziyada, Farris / McManus, I C

    BMC medicine

    2020  Volume 18, Issue 1, Page(s) 126

    Abstract: Background: What subjects UK medical schools teach, what ways they teach subjects, and how much they teach those subjects is unclear. Whether teaching differences matter is a separate, important question. This study provides a detailed picture of ... ...

    Abstract Background: What subjects UK medical schools teach, what ways they teach subjects, and how much they teach those subjects is unclear. Whether teaching differences matter is a separate, important question. This study provides a detailed picture of timetabled undergraduate teaching activity at 25 UK medical schools, particularly in relation to problem-based learning (PBL).
    Method: The Analysis of Teaching of Medical Schools (AToMS) survey used detailed timetables provided by 25 schools with standard 5-year courses. Timetabled teaching events were coded in terms of course year, duration, teaching format, and teaching content. Ten schools used PBL. Teaching times from timetables were validated against two other studies that had assessed GP teaching and lecture, seminar, and tutorial times.
    Results: A total of 47,258 timetabled teaching events in the academic year 2014/2015 were analysed, including SSCs (student-selected components) and elective studies. A typical UK medical student receives 3960 timetabled hours of teaching during their 5-year course. There was a clear difference between the initial 2 years which mostly contained basic medical science content and the later 3 years which mostly consisted of clinical teaching, although some clinical teaching occurs in the first 2 years. Medical schools differed in duration, format, and content of teaching. Two main factors underlay most of the variation between schools, Traditional vs PBL teaching and Structured vs Unstructured teaching. A curriculum map comparing medical schools was constructed using those factors. PBL schools differed on a number of measures, having more PBL teaching time, fewer lectures, more GP teaching, less surgery, less formal teaching of basic science, and more sessions with unspecified content.
    Discussion: UK medical schools differ in both format and content of teaching. PBL and non-PBL schools clearly differ, albeit with substantial variation within groups, and overlap in the middle. The important question of whether differences in teaching matter in terms of outcomes is analysed in a companion study (MedDifs) which examines how teaching differences relate to university infrastructure, entry requirements, student perceptions, and outcomes in Foundation Programme and postgraduate training.
    MeSH term(s) Curriculum/standards ; Education, Medical, Undergraduate/organization & administration ; Female ; Humans ; Male ; Surveys and Questionnaires ; United Kingdom
    Language English
    Publishing date 2020-05-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2131669-7
    ISSN 1741-7015 ; 1741-7015
    ISSN (online) 1741-7015
    ISSN 1741-7015
    DOI 10.1186/s12916-020-01571-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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