A rapid research needs appraisal methodology to identify evidence gaps to inform clinical research priorities in response to outbreaks—results from the Lassa fever pilot
Notice bibliographique
Résumé
BACKGROUND: Infectious disease epidemics are a constant threat, and while we can strengthen preparedness in advance, inevitably, we will sometimes be caught unaware by novel outbreaks. To address the challenge of rapidly identifying clinical research priorities in those circumstances, we developed and piloted a protocol for carrying out a systematic, rapid research needs appraisal (RRNA) of existing evidence within 5 days in response to outbreaks globally, with the aim to inform clinical research prioritization. METHODS: The protocol was derived from rapid review methodologies and optimized through effective use of pre-defined templates and global time zones. It was piloted using a Lassa fever (LF) outbreak scenario. Databases were searched from 1969 to July 2017. Systematic reviewers based in Canada, the UK, and the Philippines screened and extracted data using a systematic review software. The pilot was evaluated through internal analysis and by comparing the research priorities identified from the data, with those identified by an external LF expert panel. RESULTS: The RRNA pilot was completed within 5 days. To accommodate the high number of articles identified, data extraction was prioritized by study design and year, and the clinical research prioritization done post-day 5. Of 118 potentially eligible articles, 52 met the data extraction criteria, of which 46 were extracted within the 5-day time frame. The RRNA team identified 19 clinical research priorities; the expert panel independently identified 21, of which 11 priorities overlapped. Each method identified a unique set of priorities, showing that combining both methods for clinical research prioritization is more robust than using either method alone. CONCLUSIONS: This pilot study shows that it is feasible to carry out a systematic RRNA within 5 days in response to a (re-) emerging outbreak to identify gaps in existing evidence, as long as sufficient resources are identified, and reviewers are experienced and trained in advance. Use of an online systematic review software and global time zones effectively optimized resources. Another 3 to 5 days are recommended for review of the extracted data and to formulate clinical research priorities. The RRNA can be used for a "Disease X" scenario and should optimally be combined with an expert panel to ensure breadth and depth of coverage of clinical research priorities.
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 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 | 0,106 | 0,209 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,005 |
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 ».