A Critical Appraisal of the Statistical Approaches Used in Within-Individual Observations in Hand Surgery
Bibliographic record
Abstract
Hand surgery studies often include data from multiple hands, digits, or joints from 1 individual without using appropriate statistical approaches to assess within-individual observations, allowing for potential bias regarding treatment effects. We critically appraised the statistical methods used among studies, including dependent observations in hand surgery literature. All publications from the year 2020 to 2022 were retrieved from PubMed for 5 hand surgery journals. Studies containing ≥5 participants who performed a hand intervention in the operating theater were included. The proportion of patients with nonindependent observations and the proportion of nonindependent observations were calculated. A total of 10 128 articles were screened, of which a total of 465 studies were identified. Of these, 124 studies (27%) included multiple hands, joints, or digits from 1 individual. Only 79 (64%) studies provided data on the number of the digits, hands, and joints from a given patient. Of these, the proportion of patients with nonindependent observations was 14%. The proportion of nonindependent observations was 26%. Sixty-seven percent of articles did statistical comparisons between groups, but only 14.5% used methodological adjustments for within-patient relationships. Of the 71 studies that did not do proper statistical adjustments, 63 (88.7%) reported at least one significant result. In conclusion, there is a significant amount of nonindependent observations from single individuals and limited studies accounting for multiple observations in hand surgery literature. Most studies that did not do statistical adjustments for nonindependent observations still reported a significant finding, which raises the risk of bias.
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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 | 0.419 | 0.749 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.049 | 0.026 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| 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".