Notice bibliographique
Résumé
Professor Mudge and colleagues have presented a strong case for the use of meta-analysis in providing the best evidence on which to base health care decisions. They have proposed several benefits and highlighted the limitations of meta-analysis. While we concur with all the listed limitations, we feel that the following benefits they proposed need to be presented with caution. We agree that this is one of the key potential benefits of meta-analysis; however, caution needs to be exercised on two points regarding power. First, large does not always imply better. A very large sample size can lead to a detection of small and non-clinically important effect sizes as statistically significant. The size of the summary effect should be interpreted clinically before it is interpreted statistically. Second, while there is a general perception that meta-analyses always have high power to detect main effects, this is not necessarily true. Borenstein et al. [1] makes the point that in the Cochrane Database of Systematic Reviews, the median number of trials included in a review is six. When a review includes a small number of trials, the power to detect a moderate effect size may be low, especially if the random effects rather than the fixed effects model is used to compute the summary effect. This is because in the random effects model, power depends on both the within-studies error and between-studies variation. A random effects meta-analysis will only have more power than the individual studies that comprise it if the effect sizes are consistent across studies and there are a substantial number of studies. It is possible to have low power even if there are thousands of participants included in the meta-analysis [1]. This is good as a global intent of meta-analysis, but without author and journal adherence to reporting guidelines, it is doubtful whether such transparency can be achieved. The real question here is how do we ensure that the primary studies are transparent, even if there is transparency in the reporting of meta-analysis? While it is true that meta-analyses can provide greater generalizability, there are cases where the evidence has been clearly concentrated in certain regions of the world and yet generalized to others. For example, paediatric clinical trials are well known to be lacking in countries with the greatest burden of disease, and a low correlation between the number of trial participants and disease burden has been reported, especially in low-income countries [2]. Meta-analysis refers to the statistical tools to summarize evidence. Currently the methods do not include ways to disseminate such evidence. Greenhalgh et al. [3] point out many problems that hinder knowledge translation, including that the evidence must be presented in a form that is interpretable to those who need it, including the public and clinicians of limited statistical literacy. Moreover, evidence-based medicine's greatest challenge has been in getting evidence into policy and practice, leading to ‘widespread scepticism concerning its impact on practice’ [4].
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 | 0,009 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,006 |
| Communication savante | 0,013 | 0,006 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,174 | 0,104 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,017 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».