Bibliographic record
Abstract
In the early 1990s, when I was completing my residency training, general internal medicine in Canada was facing an existential crisis. From one direction, medical subspecialties were taking over as the preferred pathway for consultations from primary care physicians. A typical example was that a patient would be under the care of a cardiologist for their heart failure, an endocrinologist for their diabetes, and a nephrologist for their renal insufficiency. From the other direction, general internal medicine was being squeezed by the ascent of family medicine as a specialty in of itself, staking a greater claim to the generalist domain. As a PGY-4 in general internal medicine at the University of Toronto, I could count on one hand the number of faculty who called themselves general internists and there were even fewer trainees in this field. I distinctly recall one faculty member lamenting that he had received only one outpatient consultation request from a family physician – in the entire year! Early on in my career, when I was moonlighting as the in-house internist covering the busy emergency departments of large community hospitals, patients admitted to hospital would be siphoned off the next morning to the subspecialist closest to the admitting diagnosis and, if needed, other subspecialists were called in to help with secondary medical problems. Given these considerations, it was not surprising that general internal medicine was one of the least desirable destinations for residents completing their core internal medicine training. Indeed, the options for a career in general internal medicine were rather limited and included practicing in a remote geographic area, were sub-specialists were lacking, or pursuing a hospital-based academic career where the scope of practice reserved for general internists alone was rather narrow (e.g., staffing a perioperative consultation team).
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".