A pilot validation study of crowdsourcing systematic reviews: update of a searchable database of pediatric clinical trials of high-dose vitamin D
Notice bibliographique
Résumé
BACKGROUND: Completing large systematic reviews and maintaining them up to date poses significant challenges. This is mainly due to the toll required of a small group of experts to screen and extract potentially eligible citations. Automated approaches have failed so far in providing an accessible and adaptable tool to the research community. Over the past decade, crowdsourcing has become attractive in the scientific field, and implementing it in citation screening could save the investigative team significant work and decrease the time to publication. METHODS: Citations from the 2015 update of a pediatrics vitamin D systematic review were uploaded to an online platform designed for crowdsourcing the screening process (http://www.CHEORI.org/en/CrowdScreenOverview). Three sets of exclusion criteria were used for screening, with a review of abstracts at level one, and full-text eligibility determined through two screening stages. Two trained reviewers, who participated in the initial systematic review, established citation eligibility. In parallel, each citation received four independent assessments from an untrained crowd with a medical background. Citations were retained or excluded if they received three congruent assessments. Otherwise, they were reviewed by the principal investigator. Measured outcomes included sensitivity of the crowd to retain eligible studies, and potential work saved defined as citations sorted by the crowd (excluded or retained) without involvement of the principal investigator. RESULTS: A total of 148 citations for screening were identified, of which 20 met eligibility criteria (true positives). The four reviewers from the crowd agreed completely on 63% (95% CI: 57-69%) of assessments, and achieved a sensitivity of 100% (95% CI: 88-100%) and a specificity of 99% (95% CI: 96-100%). Potential work saved to the research team was 84% (95% CI: 77-89%) at the abstract screening stage, and 73% (95% CI: 67-79%) through all three levels. In addition, different thresholds for citation retention and exclusion were assessed. With an algorithm favoring sensitivity (citation excluded only if all four reviewers agree), sensitivity was maintained at 100%, with a decrease of potential work saved to 66% (95% CI: 59-71%). In contrast, increasing the threshold required for retention (exclude all citations not obtaining 3/4 retain assessments) decreased sensitivity to 85% (95% CI: 65-96%), while improving potential workload saved to 92% (95% CI: 88-95%). CONCLUSIONS: This study demonstrates the accuracy of crowdsourcing for systematic review citations screening, with retention of all eligible articles and a significant reduction in the work required from the investigative team. Together, these two findings suggest that crowdsourcing could represent a significant advancement in the area of systematic review. Future directions include further study to assess validity across medical fields and determination of the capacity of a non-medical crowd.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,385 | 0,208 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,009 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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; les deux têtes enseignantes 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 ».