Characteristics, results, and reporting of contemporary surgical trials: A systematic review and analysis
Bibliographic record
Abstract
INTRODUCTION: The complexities and risks inherent to the field of surgery and surgical interventions present unique challenges to the design and analysis of surgical randomized controlled trials (RCT). Prior studies have investigated the practical and methodologic challenges posed by surgical RCTs. To date, however, a comprehensive analysis of the contemporary literature across multiple surgical subspecialties does not exist. In this descriptive analysis, we set out to characterize surgical RCTs over the past 10 years across six major surgical specialties. METHODS AND ANALYSIS: A literature search by a medical librarian will be performed to identify all surgical randomized clinical trials published between January 2009 and December 2019 in the two journals with the highest impact factor for six surgical specialties as well as two large general medicine journals. Two reviewers will independently screen the citations retrieved from the literature search and extract data according to a previously described protocol via a pre-defined data collection form. Categorical variables will be reported as counts and percentages. Following assessment of normality, continuous variables will be reported as mean (standard deviation) or median (inter-quartile range). Based on normality of data, independent t-test or the Mann-Whitney U test will be used to compare continuous variables and chi-square and Fisher's exact tests to compare categorical variables. Comparisons across multiple sets will be performed using ANOVA or Kruskak-Wallis tests. Two-sided significance testing will be used and a p-value <0.05 will be considered significant without adjustment for multiple testing. All analyses will be performed using SPSS version 24 and R within RStudio. PROSPERO (ID number: 162797). ETHICS AND DISSEMINATION: There are no ethical concerns directly pertinent to this systematic review. The retrieved data will be made available upon request. The study will be written in English and submitted for publication in a peer-reviewed journal.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.206 | 0.530 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.034 | 0.043 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".