Discrepancies between Registered and Published Primary and Secondary Outcomes in Randomized Controlled Trials within the Plastic Surgery Literature: A Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Recent studies have identified a high incidence of discrepancy between registered and published outcomes in registered medical and surgical randomized controlled trials. This has not yet been studied in the plastic surgery literature. METHODS: The authors systematically assessed plastic surgery randomized controlled trials published between 2012 and 2016 in seven high-impact plastic surgery journals. Data were collected from the registration website and published articles using a standardized data extraction form. RESULTS: A total of 145 randomized controlled trials were identified, with a 39 percent trial registration rate (n = 57). Forty-nine trials were included in the final analysis. Forty-three (88 percent) had a discrepancy between registered and published outcomes: 26 (53 percent) for primary outcome(s), and 39 (80 percent) for secondary outcome(s). The number of discrepancies in an individual trial ranged from one to seven for primary outcomes and one to 12 for secondary outcomes. Aesthetic surgery had the largest number of trials with outcome discrepancies (n = 15). The prevalence of unreported registered outcomes was 13 percent for primary outcomes and 38 percent for secondary outcomes. Registered nonsignificant primary outcomes were published as nonsignificant secondary outcomes in 30 percent of trials. Publishing new nonregistered secondary outcomes (65 percent) and changing the assessment timing of published primary outcomes (61 percent) were the most common types of discrepancies. Discrepancies favored a statistically significant positive outcome in 19 (44 percent) of the 43 trials with an outcome discrepancy. Discrepancies that resulted in published outcomes with improved patient relevance were found in eight trials (16 percent) for primary outcome discrepancies and 14 trials (29 percent) for secondary outcome discrepancies. CONCLUSIONS: The plastic surgery literature has high rates of discrepancies between registered and published trial outcomes. Outcome reporting discrepancy is even more problematic for secondary outcomes, an area of analysis that has previously been poorly studied. The high rate of discrepancy change favoring a statistically significant outcome and more patient-relevant outcomes may indicate the pressure to demonstrate significant results to be accepted for publication in high-impact journals.
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 | 0,545 | 0,910 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,201 | 0,026 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,005 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».