Bibliographic record
Abstract
s a medical student in your early months, it's difficult to disagree with the notion of whole person care.Of course, all doctors should see the patient before them not as a biophysiological problem to be solved, but as a person with a family, friends, a sense of self, a sense of dignity.This seemed so obvious as to be almost banal, and I often took these lessons for granted.This changed -quickly and dramatically -when I began the clinical stage of my training.The transformative experience I wish to discuss involves a patient I will call Mrs. Hassan.Mrs. Hassan was a librarian in her early sixties, almost the same age as my mother.She had a reserved demeanor and spokeFrench with an accent.The first time I saw her, she was unconscious.One surprising thing about operating rooms is how cold they are.It was my first time on the surgical side of the OR (I had been in anesthesia the month before), and I felt that distinct blend of caffeine-induced focus and early-morning fatigue.I clumsily scrubbed in, amid a buzz of orderlies, nurses, residents, fellows, A On lifesaving care and the necessity of dignity: The story of Mrs.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".