Retrospective Data Quality Audits of the Harvard Six Cities and American Cancer Society Studies
Bibliographic record
Abstract
The Harvard Six Cities (6-Cities) and American Cancer Society (ACS) studies are longitudinal cohort mortality studies of large populations that provided important information about the human health effects associated with long-term exposure to fine particulate air pollution. Possible changes to federal regulation of particulates prompted a review of data collection methods, analysis, and reported results from these two studies. This article describes the methodology used to conduct quality assurance audits of both studies and summarizes the audit findings. Statistically based, randomly selected samples of 250 health questionnaires and 250 death certificates from each study were audited against data from analysis files. In cases where study-specific data could not be located, validation was performed using information and data from other sources. Some errors were found in programming and data transformation in both studies, but none affected the results of the original investigations. Both audits confirmed that the published studies are an accurate representation of the collected data. The audits also underscored the importance of adequate attention to documentation and record-keeping practices during the conduct of all studies and proper archiving at their conclusion.
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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 | 0.253 | 0.458 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier 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".