Bibliographic record
Abstract
Humanism is a core value of the medical profession and the reason most of us chose to pursue a career in medicine [ 1 ]. Humanism includes the attitudes and behaviors that demonstrate interest in and respect for patients’ psychological, social, and spiritual concerns and values. Many have written about the importance of humanism in our profession. Although living and practicing in a world far different from our own, much can be learned about a humanistic approach to medicine through the life and work of Sir William Osler (1849–1919) ( Figure 13.1A ). Osler was a preeminent physician of the 19th and early 20th centuries, considered to be the most influential physician in the emergence of science-based medicine. Osler’s influence extended far beyond the four medical schools he served (McGill, Penn, Johns Hopkins [one of its founders] and Oxford). Indeed, Osler’s emphasis on clinical bedside medicine shaped the ways in which medicine has been taught ever since. Osler saw medicine in its wider scope with the right and even the duty to be concerned with the human condition as a whole. Osler’s philosophy of medicine had four major components: [ 1 ] biologic reductionism about disease; [ 2 ] a scientific approach to clinical diagnosis; [ 3 ] therapeutic conservatism; and, importantly, [ 4 ] a humanistic approach to the patient [ 2 ].
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".