Complication Reporting in Orthopaedic Trials
Bibliographic record
Abstract
BACKGROUND: The nature and frequency of complications during or after orthopaedic interventions represent critical clinical information for safety evaluations, which are required for the development or improvement of orthopaedic care. The goal of this systematic review was to check whether essential data regarding the assessment of the prevalence, severity, and characteristics of complications related to orthopaedic interventions are consistently provided by the authors of papers on randomized controlled trials. METHODS: Five major peer-reviewed orthopaedic journals were screened for randomized controlled trials published between January 2006 and July 2007. All relevant papers were obtained, anonymized, and evaluated by two external reviewers. A checklist consisting of three main parts (definition, evaluation, and reporting) was developed and applied for the assessment of complication reporting. The results were stratified into surgical and nonsurgical categories. RESULTS: One hundred and twelve randomized controlled trials were identified. Although complications were included as trial outcomes in two-thirds of the studies, clear definitions of anticipated complications were provided in only eight trials. In 83% of the trials, the person or group assessing the complications was not identified. No trial involved a data safety review board for assessment and classification of complications. CONCLUSIONS: The lack of homogeneity among the published studies that we reviewed indicates that improvement in the reporting of complications in orthopaedic clinical trials is necessary. A standardized protocol for assessing and reporting complications should be developed and endorsed by professional organizations and, most importantly, by clinical investigators.
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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 | 0.586 | 0.873 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.032 | 0.035 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".