52 Strengthening the evidence in exercise sciences initiative (SEES initiative): a prospective project based on openness, surveillance, and feedback
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,486 | 0,274 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,006 | 0,003 |
| Études des sciences et des technologies | 0,006 | 0,007 |
| Communication savante | 0,011 | 0,009 |
| Science ouverte | 0,005 | 0,029 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».