Patterns and trends in quality of response rate reporting in case-control studies of cancer
Bibliographic record
Abstract
Purpose: We assessed the quality of reporting of response rates in published case-control studies of cancer over the past fourdecades.Methods: We reviewed all case-control studies of cancer published in twelve major epidemiology, public health, and generalmedicine journals in four publication periods (1984-86, 1995, 2005, and 2013). Information on study base ascertainment, datacollection methods, population characteristics, response rates, and reasons for non-participation was extracted. Quality of responserate reporting was assessed based on the amount of pertinent information reported, and in particular, numbers of non-participantsby reasons for non-participation. We calculated subject response rates by quality of response rate reporting.Results: A total of 370 studies met the eligibility criteria, yielding a total of 370 case series and 422 control series. Overall,the quality of reporting of response rate and reasons for non-participation was poor. There was a tendency for better quality ofreporting of case series, followed by population control series, and lastly by medical source control series. Quality of reportingdeclined from 1995 to 2013.Conclusion: The reporting of relevant information on response rates in case-control studies of cancer has been rather poor, and ithas not improved over time. This compromises our ability to assess validity of studies’ findings.
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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 | 0.475 | 0.704 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".