Bibliographic record
Abstract
To the Editor: In my recent commentary,1 I had noted some confusion over the nomenclature for measures of the potential impact of an intervention on risk in a community. I underestimated the complete range of terminology, however. Soon after my commentary appeared, Noel Weiss pointed out to me that a measure Tom Koepsell and he had called attributable risk to the population (PAR) in their 2003 textbook2 is identical to attributable community risk (ACR), as used in MacMahon et al3 and my commentary.1 Koepsell and Weiss refer to the measure commonly called PAR as attributable risk to the population percent (PAR%) and clearly indicate the distinction between the questions addressed by the 2 measures.2 The percent in “PAR%” refers to the percentage of cases attributable to the exposure, not the percentage of the population who develop the disease due to the exposure. I also underestimated the complete range of confusion. Although Professor Weiss taught me PAR and PAR% in class in 1977, I did not appreciate the importance of the distinction for a quarter century. Sholom Wacholder Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda, MD [email protected]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.014 |
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; both teacher heads agree on what is shown here.
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".