When is a case-control study not a case-control study?
Bibliographic record
Abstract
OBJECTIVE: There is confusion in the rehabilitation literature about case-control studies because terms such as "cases" and "controls", used to refer to the subjects in the study, are confused with the design of the study. The aim of this study was to estimate the extent to which the label "case-control study" is misused in the rehabilitation literature and in the literature of other health disciplines. DESIGN: A structured review revealed 7 rehabilitation journals, which, during the period 2000-2006, published 86 research articles in which the key word "case-control" or "case control" appeared in the title or abstract. For comparison purposes, other English language journals whose titles began with "Archives of" were also searched. RESULTS: The proportion of mislabeled case-control studies in rehabilitation journals was 97% (83 of 86 studies were mislabeled). In contrast, 34% (76 of 221) of case-control studies published in the sample of non-rehabilitation journals were found to be mislabeled. The most frequent type of rehabilitation study misclassified as case-control was a cross-sectional study (56/86) followed by intervention studies (13/86). DISCUSSION: The extent of mislabeling indicates that the case-control design is poorly understood by the rehabilitation community. This is not solely an issue of semantics; mislabeling led to misinterpretation of findings. CONCLUSION: In rehabilitation, the research questions answered by case-control studies, regarding the etiology of health events, are rarely posed. Rehabilitation researchers must be attentive to issues of design and report correctly on design in publications.
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 | 0.695 | 0.866 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.022 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".