Bibliographic record
Abstract
Healthcare providers require prior consent to treat patients. Consent can be different for legal purposes, and be expressed in different ways. Simple consent affords providers protection from liability for assault, but negligence can arise if the consent is inadequately informed. Providers cannot coerce or improperly induce consent; patients’ agreement that a provider wrongly influences is compliance, not true consent. Attempts to rescue patients in peril may be lawful on the presumption of their implied consent, unless patients negate the presumption. In special cases, laws may require that consent be written, but generally consent can be given by speech or conduct. Informed consent depends on patients’ comprehension, but consent for treatment of uncomprehending patients may come from third parties, including legally recognized substitutes or judges. There may be legal limits to reproductive procedures to which patients may consent, under laws that can be respectfully tested, but have to be obeyed.
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 | 0.055 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".