Bibliographic record
Abstract
Not much: guidelines must consider cost effectiveness From air travel to patient safety to coronary heart disease prevention, people strive to reduce risk to zero. We know that zero risk is unattainable, yet we pursue perfection. It may be useful to hold perfection as an ideal,1 but there can be great harm in trying to achieve it because near perfection often imposes near infinite costs. The closer we get to perfect risk reduction, the more likely it becomes that we could have got a better bang for our preventive buck somewhere else. This applies across all activities–and needs to be heeded in health care as anywhere else. For example, air travel is already much safer than most other forms of travel, so £10m ($17m; €14m) spent on road safety would save far more life years than £10m put into tightening airport security. Yet since September 11 much new spending has gone into airport security. In health too we often see a rush to perfection without regard for costs. Here are three examples. Firstly, universal precautions to prevent worksite transmission of HIV to healthcare workers have been widely implemented, yet cost …
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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.037 | 0.026 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".