Deficiencies in the reporting quality of RCTs in neurosurgery: How can we do better?
Bibliographic record
Abstract
Background: Deficiencies in design and reporting of randomized controlled trials (RCTs) limit their validity. The quality of recent RCTs in neurosurgery was analyzed to assess adequacy of design and reporting. Methods: A high-yield search of the MEDLINE and EMBASE databases (2000-present) was conducted. The CONSORT and Jadad scales were used to assess the quality of design/reporting. A PRECIS-based scale was used to designate studies on the pragmatic-explanatory continuum. Spearman’s test was used to assess correlations. Regression analysis was used to assess associations. Results: Sixty-one articles were identified. Vascular was the most common sub-specialty (37%). The median CONSORT and Jadad scores were 36 (IQR 27.5-39) and 3 (IQR 2-3). Blinding, sample size calculation and allocation concealment were most deficiently reported. The quality of reporting did not correlate with the study impact. The majority of studies (83%) had pragmatic objectives; while pragmatic studies had compatible design factors, trials with explanatory objectives were less successful. Conclusions: The prevalence and quality of neurosurgical RCTs is low. Many study designs are not compatible with stated objectives. Given the role of RCTs as one of the highest levels of evidence, it is critical to improve on their methodology and reporting. Alternative methodologies merit discussion.
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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 | 0.743 | 0.892 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.010 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".