Bibliographic record
Abstract
I n the early part of the 20th century, a periodic health exam for prevention purposes became widespread. The notion of seeing your doctor on a regular basis, as a checkup, somehow gained acceptance during an era when most still believed “if it ain't broken, don't fix it.” The American Medical Association (AMA) endorsed this prevention exam mindset in 1922, in part because life insurance policyholders who had this exam had a decrease in mortality: a classic example of selection bias. Two Canadians, Frame and Carlson, wrote one of the first critical reviews of a component of prevention—screening—in 1975.1 What followed, in the form of a Canadian Task Force on the Periodic Health Exam (1979) and, a decade later, a U.S. Preventive Task Force on the Periodic Health Exam (1989), was a flurry of analysis about the quality and quantity of information and recommendations that …
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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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".