52 Strengthening the evidence in exercise sciences initiative (SEES initiative): a prospective project based on openness, surveillance, and feedback
Bibliographic record
Abstract
Objectives The questionable quality of evidence has been increasingly documented in medical research, denoting that scientific findings may ultimately be, at least, of limited usability. Some of the countermeasures to reduce the waste of research include (i) resources for transparency such as public repositories and registry platforms; and (ii) initiatives to improve research communication (e.g., The EQUATOR Network) or promote education on methodological issues (e.g., The Catalogue of Bias). Although such resources are fundamental to improve biomedical research as a whole, many research fields still neglect the need to improve the evidence quality. Therefore, we propose a discipline-based initiative to foster awareness for better quality evidence and increase the adherence to widely recommended methodological and reporting practices. Herein, we present The Strengthening the Evidence in Exercise Sciences Initiative (SEES Initiative) by which we will prospectively conduct surveillance of published articles and feedback to study authors and journal editors. Method Our rationale and methods are presented in a protocol article whereas detailed assessment guidance is described in a manual of standardised procedures. Both documents are available on our website (www.sees-initiative.org). We conduct our processes at a monthly-basis, as follows: (i) a pre-assessment stage comprises the use of sensitive filters to search newly-published articles reporting randomised clinical trials (RCTs) or systematic review with meta-analyses (SRMAs) in nine exercise sciences journals and five general medicine journals; (ii) at the assessment stage, RCTs and SRMAs having a research question related to sport, exercise, or physical activity are assessed in duplicate by independent RCT and SRMA teams based on 30+ items derived from established tools or recommendations; (iii) at the dissemination stage, we carry out the analyses, report results on the website and to study authors and journal editors, as well as deposit prespecified files at a public repository (OSF). Results We completed a census to characterize the types of studies published in 2018 by the nine-journal cohort in exercise sciences. From a total of 3,205 individual references, we respectively classified 277 (9%; min-max range, 5 to 92) and 248 (8%; min-max range, 6 to 72) articles as RCTs and SRMAs. Currently, our three-stage process is ongoing and, from the two first months of surveillance, 38 RCTs and 27 SRMAs were eligible for analysis. We consolidated a comprehensive assessment using items from CONSORT 2010 and TIDieR checklist to appraise RCTs and from PRISMA, AMSTAR-2, and ROBIS to appraise SRMAs. In addition to full study reports with our assessment for all items, we proposed aggregated results using seven components (aggregating from 4 to 11 assessed items) that relate to: transparency, completeness, methodological rigor, participants, interventions/exposures, outcome, and critical appraisal. Conclusions Inspired by the Mertonian principles and Doug Altman’s wisdom, the SEES Initiative is a living, scalable, open project to promote adequate reporting, feedback stakeholders toward increased research uptake, and disrupt/denounce inadequate practices whenever necessary.
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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 | 0.486 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".