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Record W2517080491 · doi:10.9778/cmajo.20160062

Research projects in the Surgeon-Scientist and Clinician-Investigator programs at the University of Toronto (1987-2016): a cohort study

2016· article· en· W2517080491 on OpenAlexaffvenueabout
Neil M. Goldenberg, Benjamin E. Steinberg, James T. Rutka, Robert Chen, Val Cabral, Norman D. Rosenblum, András Kapùs, Warren L. Lee

Bibliographic record

VenueCMAJ Open · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedical educationPopularityMedicinePopulationPsychologyFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Physicians have traditionally been at the forefront of medical research, bringing clinical questions to the laboratory and returning with ideas for treatment. However, we have anecdotally observed a decline in the popularity of basic science research among trainees. We hypothesized that fewer resident physicians have been pursuing basic science research training over time. METHODS: We examined records from residents in the Surgeon-Scientist and Clinician-Investigator programs at the University of Toronto (1987-2016). Research by residents was categorized independently by 2 raters as basic science, clinical epidemiology or education-related based on the title of the project, the name of the supervisor and Pubmed searches. The study population was divided into quintiles of time, and the proportion pursuing basic science training in each quintile was calculated. RESULTS: = 0.005). INTERPRETATION: Significantly fewer residents in the Surgeon-Scientist and Clinician-Investigator Programs at the University of Toronto are pursuing training in the basic sciences as compared with previous years.

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)0.020
metaresearch head score (Gemma)0.015
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 score0.123
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.286
GPT teacher head0.493
Teacher spread0.207 · 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

Citations8
Published2016
Admission routes3
Has abstractyes

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