Bibliographic record
Abstract
Rehabilitation professionals rarely ask questions about the etiology of health events or outcomes and may not have formal training or relevant experience in the design of studies whose intent is to identify causal factors. The case-control study, which is one design used to answer questions about etiology, is particularly difficult to understand and research has shown that this study design label is often used incorrectly. This paper outlines the main features of case-control studies, with a particular focus on sampling strategies. The goal is to educate clinical rehabilitation colleagues about the fundamental principles of this powerful epidemiologic design. Examples illustrate how the parameters of cumulative incidence, incidence-density, and prevalence are estimated and the effect of sampling strategy on these parameters. Also shown is how sampling strategy affects conclusions drawn about the effects of an exposure on outcome. Even when used appropriately, case-control studies are methodologically complex to design and analyze to ensure an unbiased answer to the research question. The hypothetical and real-life examples given here could be used as course material to educate rehabilitation researchers.
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 | 0.646 | 0.825 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.030 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".