Assessing use of surrogate outcome measures in randomized controlled trials investigating venous thromboembolism prophylaxis
Bibliographic record
Abstract
Abstract Background: Outcome measures in randomized controlled trials (RCTs) can be classified as patient-important, surrogate and composite outcomes. Venous thromboembolism (VTE) prophylaxis trials often use surrogate or composite outcome measures, often involving radiographically detected and clinically asymptomatic VTE. The clinical relevance of such outcomes is controversial.Objective: To establish the prevalence of surrogate outcome measures in VTE prophylaxis trials.Methods: A comprehensive search of MEDLINE, Embase and Cochrane (CENTRAL) databases from January 2000 to October 2020 was conducted. Any English-language VTE prophylaxis RCT with n>150 was included. Baseline characteristics, outcome measure, acknowledgement of limitations, and presence of VTE vs bleeding risk discussions were recorded. Binary logistic regression analysis was done to assess the relationship of impact factor, citation count, and sample size with surrogate outcome use. Results: 209 studies were included. 170 (81%) studies used a surrogate outcome measure. Of these studies, 34 (20%) acknowledged this as a potential limitation and 152 (89%) discussed bleeding vs thrombosis risk. There was no statistically significant relationship between citation count (ꞵ1OR=0.99; 95%CI=0.99-1.00; p=0.248) or sample size (ꞵ1OR=0.99, 95%CI=0.99-1.00, p=0.320) and use of a surrogate outcome compared with use of a clinical outcome. There was a statistically significant relationship between increased impact factor and use of a surrogate outcome (ꞵ1OR=0.98, 95%CI=0.97-0.99, p<0.05).Conclusion: Use of surrogate outcomes is prevalent in VTE prophylaxis literature and is rarely acknowledged. While using patient-important outcome measures may not be feasible in all settings, we recommended clinicians acknowledge the limitation of surrogate outcomes, especially when discussing bleeding vs thrombosis risk.
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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 | 0.694 | 0.875 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".