Bibliographic record
Abstract
Dr Jackson and Dr Warner (December 2002 JRSM1) point to large gaps in doctors' knowledge of consent and capacity. Two aspects of their project should, however, be questioned. First, the concept of a patient in whom capacity issues may be relevant is problematic. As White has pointed out,2 even if legal and ethical standards support a positive assumption regarding the decision-making competence of the patient, a physician, when seeking approval of a treatment plan, should always make sure that the patient possesses adequate decision-making abilities. Second, whatever the legal position, physicians and other healthcare professionals remain the ones who are responsible for the first-hand evaluation. When a court must decide on a decision-making-capacity issue, the judge depends almost exclusively on the evaluation made by the physician. Research must then look to the process of decision-making capacity of the patient as affected by special diseases. Marson et al. did this in Alzheimer's disease.3, 4, 5 Other researchers, myself included, are turning their attention to the ethico-clinical judgment of the physician. Doctors evaluate, almost daily, decision capacity issues. Taking into account the work of Schon, we hypothesize that physicians have expert knowledge of these matters, even if it is unconscious. Issues on ability to consent represent a challenge to every physician on a clinical ward, since all healthcare professionals are engaged in what should be an informed consent procedure with their patients.
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 | 0.023 | 0.251 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 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".