The Importance of Pragmatic Study Design to the Scholarly Influence of Surgical Hip Fracture Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Surgical randomized controlled trials (RCTs) have potential drawbacks, leading some to question their role in filling the information gap in orthopaedic surgery. Pragmatism in study design was introduced to increase the clinical applicability of study results. The purpose of this study was to examine how pragmatism affects the scholarly influence of surgical RCTs. METHODS: A search for surgical hip fracture-related RCTs published between 1995 and 2015 was done. Journal impact factor, citation number, research question, significance and type of outcome, number of centers involved, and the Pragmatic-Explanatory Continuum Indicator Summary-2 level of pragmatism score were recorded for each study. Scholarly influence was estimated by a study's inclusion into orthopaedic literature or guidelines or through the study's average yearly citation rate. RESULTS: One hundred sixty RCTs were included in the final analysis. A multivariate logistic regression identified large study sample size as the only predictor of an RCT being used in clinical guidance texts. Large sample size and multicenter RCTs were predictors of high yearly citation rates. The level of pragmatism in study design did not predict scholarly influence. CONCLUSIONS: Pragmatic design is not independently associated with increased scholarly influence; however, large study sample size was the most important study characteristic affecting scholarly influence.
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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 | 0.761 | 0.912 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| 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".