Bibliographic record
Abstract
Editor—Imagine waking tomorrow to find a magic lamp by your bed, and the genie tells you that there is only one wish left. You decide to devote it to making good doctors. What kind of people would these good doctors be? We ask this question often among ourselves—a doctor embarking on his career, an active researcher approaching his peak, and a retired clinician needing geriatric care. We sometimes ask other people too. Despite the disparate vantage points, the wish lists are amazingly similar. We all want doctors who will: Respect people, healthy or ill, regardless of who they are Support patients and their loved ones when and where they are needed Promote health as well as treat disease Embrace the power of information and communication technologies to support people with the best available information, while respecting their individual values and preferences Always ask courteous questions, let people talk, and listen to them carefully Give unbiased advice, let people participate actively in all decisions related to their health and health care, assess each situation carefully, and help whatever the situation Use evidence as a tool, not as a determinant of practice; humbly accept death as an important part of life; and help people make the best possible arrangements when death is close Work cooperatively with other members of the healthcare team Be proactive advocates for their patients, mentors for other health professionals, and ready to learn from others, regardless of their age, role, or status Finally, we want doctors to have a balanced life and to care for themselves and their families as well as for others. In sum, we want doctors to be happy and healthy, caring and competent, and good travel companions for people through the journey we call life. Unfortunately, we do not have a magic lamp, and there is no genie. We must use our own skills and endeavours to make the good doctors we want and need. It is an awesome responsibility.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 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".