Bibliographic record
Abstract
Liability to err is a human, often unavoidable, characteristic. Errors can be classified as skill-based, rule-based, knowledge-based and other errors, such as of judgment. In law, a key distinction is between negligent and non-negligent errors. To describe a mistake as an error of clinical judgment is legally ambiguous, since an error that a physician might have made when acting with ordinary care and the professional skill the physician claims, is not deemed negligent in law. If errors prejudice patients' recovery from treatment and/or future care, in physical or psychological ways, it is legally and ethically required that they be informed of them in appropriate time. Senior colleagues, facility administrators and others such as medical licensing authorities should be informed of serious forms of error, so that preventive education and strategies can be designed. Errors for which clinicians may be legally liable may originate in systemically defective institutional administration.
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 | 0.056 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.048 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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".