In Reply: Guidelines for the Management of Severe Traumatic Brain Injury: 2020 Update of the Decompressive Craniectomy Recommendations
Notice bibliographique
Résumé
To the Editor: We thank the authors1 for their thoughtful commentary related to the DECRA (DECompressive CRAniectomy) and RESCUEicp (Randomized Evaluation of Surgery with Craniectomy for Uncontrollable Elevation of intracranial pressure) randomized controlled trials. We are pleased to provide further thoughts on the controversy related to the extended Glasgow Outcome Scale (GOS-E) cut-point used in the RESCUEicp trial as well as the need to improve prognostication related to the performance of secondary decompressive craniectomy. In conjunction with our analysis2 of the DECRA and RESCUEicp trials, we performed a sensitivity analysis examining the influence of the cut-points on the primary endpoints of the 2 trials. Of note, DECRA’s trial registration specified the GOS-E cut-point to be used a priori, while RESCUEicp's trial registration did not specify a GOS-E cut-point. The results of our analysis (shown in Table) demonstrate that the distinct cut-points influence the results of the DECRA study but not the RESCUEicp study. The atypical dichotomization used in the RESCUEicp study thus did not influence its primary outcome though it does alter the magnitude of the nonsignificant effect seen, as highlighted in your letter. The difference in cut-points was an impediment to comparing the 2 studies; ultimately, we abandoned efforts to directly compare the 2 trials because of their important differences. TABLE. - Results of Chi-Square Analyses Comparing Primary Outcomes of DECRA with RESCUEicp, Measured by Dichotomous GOS-E for Two Cut-Points DECRA RESCUEicp GOS-E No significant difference between treatment groups No significant difference between treatment groups 1-3 unfavorable 4-8: favorable GOS-E1-4: unfavorable5-8: favorable Significantly more patients in unfavorable outcomes group No significant difference between treatment groups Gray cells denote the published dichotomization used in reporting the primary outcome measure (6-mo GOS-E) in each study. We emphatically agree with the need to better elucidate which patients stand a high probability of achieving good outcomes from secondary decompressive craniectomy.3 The success of the CRASH (Corticosteroid Randomization After Significant Head Injury)4 and IMPACT (International Mission for Prognosis And Clinical Trials in Traumatic Brain Injury)5 prognostic models has been a significant recent advance for traumatic brain injury care. Hopefully, these efforts have blazed a trail that other prognostic efforts will be able to follow. Notably, both prognostic models depend upon very large databases with over 9 000 severe traumatic brain injury patients to overcome the marked heterogeneity of this population. It is therefore anticipated that a similarly large dataset may be needed to generate robust prognostic information related to secondary decompressive craniectomy. Funding This study did not receive any funding or financial support. Disclosures The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; un appel candidat d’une seule tête enseignante, 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 ».