Bibliographic record
Abstract
> In the fields of observation, chance favors only the prepared mind. > > — Louis Pasteur, University of Lille, December 7, 1854 What is the influence of a chance encounter in a training program? As the Rheumatology community grapples with the average age of practitioners in the mid-50s and a flat rate of already low recruitment of rheumatology trainees (RT) from internal medicine (IM) training programs, does type and timing of exposure to rheumatology have a predictive value? In an intriguing article in this issue of The Journal 1, Drs. Katz and Yacyshyn have data-mined the Canadian Post-MD Education Registry (CAPER) (which enables anonymous tracking of RT based on their IM residency training site), and examined the relationship of mere availability of a rheumatology rotation in each postgraduate year to the number of eventual RT generated. Curriculum information was obtained from the Canadian Residency Matching Service over a 3-year period; because programs were under major review during this period, no changes were likely to be implemented, assuring homogeneous atomic data. Using advanced logistic regression techniques, Katz and Yacyshyn assigned the availability of a rheumatology elective a numeric value from 0 (no chance of completing a rotation over a given month) to 1 (mandatory completion). They address program restrictions in selectivity (a choice between rheumatology and another rotation would be 0.5), and accordingly, if rheumatology were one choice among 10 rotations, the score would be 0.1. While statistical purists might argue the validity of this construct, it is an attempt to quantify existing data, as the number of rheumatology electives would not be considered a normally distributed value across IM training sites and in each postgraduate year, due to local variations in curriculum, full-time faculty equivalents, or participating community rheumatologists, as they ably demonstrate in their Table 1. There was a positive relationship (Figure 1) between postgraduate year 1 rheumatology opportunities … Address correspondence to Dr. McCarty. E-mail: gmccarty{at}mainehospital.org
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".