Appropriateness and Quality of Composite Endpoint Use and Reporting in Spine Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it