Are outcomes reported in surgical randomized trials patient-important? A systematic review and meta-analysis
Bibliographic record
Abstract
<h3>Background:</h3> The dangers of using surrogate outcomes are well documented. They may have little or no association with their patient-important correlates, leading to the approval and use of interventions that lack efficacy. We sought to assess whether primary outcomes in surgical randomized controlled trials (RCTs) are more likely to be patient-important outcomes than surrogate or laboratory-based outcomes. <h3>Methods:</h3> We reviewed RCTs assessing an operative intervention published in 2008 and 2009 and indexed in MEDLINE, EMBASE or the Cochrane Central Register of Controlled Trials. After a pilot of the selection criteria, 1 reviewer selected trials and another reviewer checked the selection. We extracted information on outcome characteristics (patient-important, surrogate, or laboratory-based outcome) and whether they were primary or secondary outcomes. We calculated odds ratios (OR) and pooled in random-effects meta-analysis to obtain an overall estimate of the association between patient importance and primary outcome specification. <h3>Results:</h3> In 350 included RCTs, a total of 8258 outcomes were reported (median 18 per trial. The mean proportion (per trial) of patient-important outcomes was 60%, and 66% of trials specified a patient-important primary outcome. The most commonly reported patient-important primary outcomes were morbid events (41%), intervention outcomes (11%), function (11%) and pain (9%). Surrogate and laboratory-based primary outcomes were reported in 33% and 8% of trials, respectively. Patient-important outcomes were not associated with primary outcome status (OR 0.82, 95% confidence interval 0.63–1.1, <i>I</i><sub>2</sub> = 21%). <h3>Conclusion:</h3> A substantial proportion of surgical RCTs specify primary outcomes that are not patient-important. Authors, journals and trial funders should insist that patient-important outcomes are the focus of study.
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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 | 0.661 | 0.730 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.277 | 0.120 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".