Appropriateness and Quality of Composite Endpoint Use and Reporting in Spine Surgery
Bibliographic record
Abstract
BACKGROUND: A composite endpoint (CEP) is a measure comprising 2 or more separate component outcomes. The use of these constructs is increasing. We sought to conduct a systematic review on the usage, quality of reporting, and appropriate use of CEPs in spine surgery research. METHODS: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Articles reporting randomized controlled trials of a spine surgery intervention using a CEP as a primary outcome were included. We assessed the quality of CEP reporting, appropriateness of CEP use, and correspondence between CEP treatment effect and component outcome treatment effect in the included trials. RESULTS: Of 2,321 initial titles, 43 citations were included for analysis, which reported on 20 unique trials. All trials reported the CEP construct well. In 85% of trials, the CEP design was driven by US Food and Drug Administration guidance. In the majority of trials, the reporting of CEP results did not adhere to published recommendations: 43% of tests that reported statistically significant results on component outcomes were not statistically significant when adjusted for multiple testing. 67% of trials did not meet appropriateness criteria for CEP use. In addition, CEP treatment effect tended to be 6% higher than the median treatment effect for component outcomes. CONCLUSION: Given that CEP analysis was not appropriate for the majority of spine surgery trials and the inherent challenges in the reporting and interpretation of CEP analysis, CEP use should not be mandated by regulatory bodies in spine surgery trials. LEVEL OF EVIDENCE: Level I. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.601 | 0.858 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".