Reporting of planned statistical methods in published surgical randomised trial protocols: a protocol for a methodological systematic review
Bibliographic record
Abstract
INTRODUCTION: Poor reporting can lead to inadequate presentation of data, confusion regarding research methodology used, selective reporting of results, and other misinformation regarding health research. One of the most recent attempts to improve quality of reporting comes from the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) Group, which makes recommendations for the reporting of protocols. In this report, we present a protocol for a systematic review of published surgical randomised controlled trial (RCT) protocols, with the purpose of assessing the reporting quality and completeness of the statistical aspects. METHODS: We will include all published protocols of randomised trials that investigate surgical interventions. We will search MEDLINE, EMBASE, and CENTRAL for relevant studies. Author pairs will independently review all titles, abstracts, and full texts identified by the literature search, and extract data using a structured data extraction form. We will extract the following: year of publication, country, sample size, description of study population, description of intervention and control, primary outcome, important methodological qualities, and quality of reporting of planned statistical methods based on the SPIRIT guidelines. ETHICS AND DISSEMINATION: The results of this review will demonstrate the quality of statistical reporting of published surgical RCT protocols. This knowledge will inform recommendations to surgeons, researchers, journal editors and peer reviewers, and other knowledge users that focus on common deficiencies in reporting and how to rectify them. Ethics approval for this study is not required. We will disseminate the results of this review in peer-reviewed publications and conference presentations, and at a doctoral independent study of oral defence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.536 | 0.682 |
| Meta-epidemiology (narrow) | 0.009 | 0.010 |
| Meta-epidemiology (broad) | 0.018 | 0.017 |
| Bibliometrics | 0.024 | 0.031 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.048 | 0.029 |
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".