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Record W2337244232 · doi:10.1097/rhu.0000000000000186

Immunology for Rheumatology Residents

2015· article· en· W2337244232 on OpenAlexaffabout
Shirley Chow, Sari M. Herman-Kideckel, Dharini Mahendira, Heather McDonald-Blumer

Bibliographic record

VenueJCR Journal of Clinical Rheumatology · 2015
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsRheumatologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Immunologic mechanisms play an integral role in understanding the pathogenesis and management of rheumatic conditions. Currently, there is limited access to formal instruction in immunology for rheumatology trainees across Canada. AIM: The aims of this study were (1) to describe current immunology curricula among adult rheumatology training programs across Canada and (2) to compare the perceived learning needs of rheumatology trainees from the perspective of program directors and trainees to help develop a focused nationwide immunology curriculum. METHODS: Rheumatology trainees and program directors from adult rheumatology programs across Canada completed an online questionnaire and were asked to rank a comprehensive list of immunology topics. A modified Delphi approach was implemented to obtain consensus on immunology topics. RESULTS: Only 42% of program directors and 31% of trainees felt the current method of teaching immunology was effective. Results illustrate concordance between program directors and trainees for the highest-ranked immunology topics including innate immunity, adaptive immunity, and cells and tissues of the immune system. However, there was discordance among other topics, such as diagnostic laboratory immunology and therapeutics. CONCLUSIONS: There is a need to improve immunology teaching in rheumatology training programs. Results show high concordance between the basic immunology topics. This study provides the groundwork for development of future immunology curricula.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.083
GPT teacher head0.444
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2015
Admission routes2
Has abstractyes

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