What Isn’t a Case-Control Study?
Bibliographic record
Abstract
BACKGROUND: Confusion exists among neurosurgeons when choosing and implementing an appropriate study design and statistical methods when conducting research. We noticed particular difficulty with mislabeled and inappropriate case-control studies in the neurosurgical literature. OBJECTIVE: To quantify and to rigorously review this issue for appropriateness in publication and to establish quality of the manuscripts using a rigorous technique. METHODS: Following a literature search, pairs drawn from 5 independent reviewers evaluated a complete sample of 125 manuscripts claiming to be case-control studies with respect to basic case-control criteria. Seventy-five papers were then subjected to a more rigorous appraisal for quality using the SIGN Methodology Checklist for case-control studies. RESULTS: Fifty publications were rejected based on basic criteria used to identify case-control design. Of the 75 subjected to quality analysis, 46 were felt to be acceptable for publication. Only 11 papers (9%) achieved the designation of high quality. Of the original 125 papers evaluated, 79 (63%) were inappropriately labeled case-control studies. CONCLUSION: Mislabeling and use of inappropriate study design are common in the neurosurgical literature. Manuscripts should be evaluated rigorously by reviewers and readers, and neurosurgical training programs should include instruction on choice of appropriate study design and critical appraisal of the literature.
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 | 0.094 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".