Up front and open, shrouded in secrecy, or somewhere in between? A Meta Research Systematic Review of Open Science Practices in Sport Medicine Research
Bibliographic record
Abstract
Abstract Objective To investigate the extent and qualitatively synthesize open science practices within research published in the top five sports medicine journals from 01 May 2022 and 01 October 2022. Design Meta-research systematic review Data Sources MEDLINE Eligibility Criteria Studies were included if they were published in one of the identified top five sports medicine journals as ranked by Clarivate. Studies were excluded if they were systematic reviews, qualitative research, grey literature, or animal or cadaver models. Results 243 studies were included. The median number of open science practices met per study was 2, out of a maximum of 12 (Range: 0-8; IQR: 2). 234 studies (96%, 95% CI: 94-99) provided an author conflict of interest statement and 163 (67%, 95% CI: 62-73) reported funding. 21 studies (9%, 95% CI: 5-12) provided open access data. 54 studies (22%, 95% CI: 17-included a data availability statement and 3 (1%, 95% CI: 0-3) made code available. 76 studies (32%, 95% CI: 25-37) had transparent materials and 30 (12%, 95% CI: 8-16) included a reporting guideline. 28 studies (12%, 95% CI: 8-16) were pre-registered. 6 studies (3%, 95% CI: 1-4) published a protocol. 4 studies (2%, 95% CI: 0-3) reported the availability of an analysis plan. 7 studies (3%, 95% CI: 1-5) reported patient and public involvement. Conclusion Sports medicine open science practices are extremely limited. The least followed practices were sharing code, data, and analysis plans. Without implementing open practices, barriers concerning the ability to aggregate findings and create cumulative science will continue to exist. What is already known Open science practices provide a mechanism for evaluating and improving the quality and reproducibility of research in a transparent manner, thereby enhancing the benefits to patient outcomes and society at large. Understanding the current open science practices in sport medicine research can assist in identifying where and how sports medicine leadership can raise awareness, and develop strategies for improvement. What are the new findings No study published in the top five sports medicine journals met all open science practices Studies often only met a small number of open science practices Open science practices that were least met included providing open access code, data sharing, and the availability of an analysis plan.
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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 | 0.138 | 0.348 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".