MétaCan
Menu
Back to cohort
Record W4387798969 · doi:10.2519/jospt.2023.12016

Up Front and Open? Shrouded in Secrecy? Or Somewhere in Between? A Meta-Research Systematic Review of Open Science Practices in Sport Medicine Research

2023· review· en· W4387798969 on OpenAlexaff
Garrett S. Bullock, Patrick Ward, Franco M. Impellizzeri, Stefan Kluzek, Tom Hughes, Charles C. Hillman, Brian R. Waterman, Kerry A. Danelson, Kaitlin Henry, Emily Barr, Kelsey M. Healy, Anu M. Räisänen, Christina Gomez, Garrett Fernandez, Jakob Wolf, Kristen F. Nicholson, Tim Sell, Ryan Zerega, Paula Dhiman, Richard D Riley, Gary S. Collins

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2023
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of Calgary
FundersCancer Research UK
KeywordsFront (military)SecrecyOpen scienceOpen researchEngineeringComputer scienceWorld Wide WebPhysicsComputer securityMechanical engineeringAstronomy

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate open science practices in research published in the top 5 sports medicine journals from May 1, 2022, and October 1, 2022. DESIGN: A meta-research systematic review. LITERATURE SEARCH: Open science practices were searched in MEDLINE. STUDY SELECTION CRITERIA: We included original scientific research published in one of the identified top 5 sports medicine journals in 2022 as ranked by Clarivate: (1) British Journal of Sports Medicine, (2) Journal of Sport and Health Science, (3) American Journal of Sports Medicine, (4) Medicine and Science in Sports and Exercise, and (5) Sports Medicine-Open. Studies were excluded if they were systematic reviews, qualitative research, gray literature, or animal or cadaver models. DATA SYNTHESIS: Open science practices were extracted in accordance with the Transparency and Openness Promotion guidelines and patient and public involvement. RESULTS: Two hundred forty-three studies were included. The median number of open science practices in each study was 2, out of a maximum of 12 (range: 0-8; interquartile range: 2). Two hundred thirty-four studies (96%, 95% confidence interval [CI]: 94%-99%) provided an author conflict-of-interest statement and 163 (67%, 95% CI: 62%-73%) reported funding. Twenty-one studies (9%, 95% CI: 5%-12%) provided open-access data. Fifty-four studies (22%, 95% CI: 17%-27%) included a data availability statement and 3 (1%, 95% CI: 0%-3%) made code available. Seventy-six studies (32%, 95% CI: 25%-37%) had transparent materials and 30 (12%, 95% CI: 8%-16%) used a reporting guideline. Twenty-eight studies (12%, 95% CI: 8%-16%) were preregistered. Six studies (3%, 95% CI: 1%-4%) published a protocol. Four studies (2%, 95% CI: 0%-3%) reported an analysis plan a priori. Seven studies (3%, 95% CI: 1%-5%) reported patient and public involvement. CONCLUSION: Open science practices in the sports medicine field are extremely limited. The least followed practices were sharing code, data, and analysis plans. J Orthop Sports Phys Ther 2023;53(12):735-747. Epub 20 October 2023. doi:10.2519/jospt.2023.12016

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 imitation

Not 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.

metaresearch head score (Codex)0.090
metaresearch head score (Gemma)0.266
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.474

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0900.266
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0140.025
Bibliometrics0.0180.018
Science and technology studies0.0020.003
Scholarly communication0.0090.011
Open science0.0030.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.936
GPT teacher head0.709
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainReproducibility
GenreReview

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".

Quick stats

Citations14
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Orthopaedic and Sports Physical TherapySame topicMeta-analysis and systematic reviewsFrench-language works237,207