Bibliographic record
Abstract
How decisions are made and patients cared for are often guided by the Golden Rule, which would have us treat patients as we would want to be treated in similar circumstances. But when patients' lived experiences and outlooks deviate substantively from our own, we stop being a reliable barometer of their needs, values, and goals. Inaccurate perceptions of their suffering and our personal biases may lead to distorted compassion, marked by an attitude of pity and therapeutic nihilism. In those instances, The Platinum Rule, which would have us consider doing unto patients as they would want done unto themselves, may be a more appropriate standard for achieving optimal person-centered care. This means knowing who patients are as persons, hence guiding treatment decisions and shaping a tone of care based on compassion and respect.
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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.026 | 0.057 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".