Bibliographic record
Abstract
When I was a medical student, I had a medicine rotation with a spectacular senior resident and a less than stellar staffperson. The resident was very smart, terrifically organized, and ran the service extremely efficiently. The staffperson had probably spent too much time away from clinical medicine. I distinctly remember team rounds in which our resident would present cases such that the staffperson appeared to make the big decisions (“I was uncertain what to do but thought that you would likely get a bone marrow biopsy”, to which the staffperson would usually nod and muse, “Good plan”). As a student, I was struck by the irony of a resident guiding the staffperson. Now that I am a staffperson, who probably spends too much time away from clinical medicine, I am struck by how cunning the staffperson was to listen carefully to the resident and accept the valid decisions. Like a wise staffperson, guidelines are supposed to help physicians practice best medicine within an environment containing an ever-increasing volume of information. Although guidelines have been successful in changing physician practice (1)(2), they have not always been successful in modifying laboratory utilization (3). There are many possible reasons to explain this. Not all physicians receive, read, or agree with particular guidelines. Because it is impossible for guidelines to consider all variations in patient populations and physician practice styles, dissonance between guideline recommendations and actual practice will always exist. Finally, information contained in guidelines has often been disseminated through other routes and has already modified the practice of receptive physicians.
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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.026 | 0.047 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".