Bibliographic record
Abstract
Healthcare practitioners face pressure on many fronts to deliver preventative medicine: disease specific interest groups (1) and both Canadian commissions on health care (2,3) stress preventative medicine as a core component of health care. These recommendations are relayed to a public who, having lost loved ones from ‘preventable’ illnesses, are often eager to engage with prevention programs (1). Preventative measures can be effective and efficient: for instance, a series of questions taking just one minute to complete can double the chances that someone who wasn’t ready to stop smoking may actually quit (4). However, preventative treatments for healthy individuals carry risks not associated with treatment of illnesses; there is more to lose and less to gain when treating healthy individuals. Preventative medicine has been criticized for being dangerously aggressive in seeking to apply to whole groups of individuals, using even the force of law in the case of vaccinations (5). Similarly, the ethics of ‘opportunistic’ preventative care for those who present with an unrelated concern is still under debate (6). Preventative medicine has been called presumptuous for its confidence that on average it will do more good than harm, and overbearing, for its attacks on those who question its benefits (5).
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 | 0.053 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.018 | 0.026 |
| Insufficient payload (model declined to judge) | 0.011 | 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".