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Enregistrement W3209904962 · doi:10.1016/j.ijsu.2021.106154

A commentary on “Comparative analysis of the efficacy of early and late surgical intervention for acute spinal cord injury: A systematic review and meta-analysis based on 16 studies” (Int. J. Surg. 2021 (94) 106098)

2021· review· en· W3209904962 sur OpenAlexaboutno aff
Teng Chen, Zongping Xiao, Jin Zhang, Liyun Jiang

Notice bibliographique

RevueInternational Journal of Surgery · 2021
Typereview
Langueen
DomaineMedicine
ThématiqueSpinal Cord Injury Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineSpinal cord injuryDecompressionMeta-analysisPerioperativeSurgeryIntensive care medicineSpinal cordInternal medicine

Résumé

récupéré en direct d'OpenAlex

Dear Editor, Traumatic spinal cord injury (SCI) with neurological impairment is a tragic event that imposes a significant burden on individuals and society. Symptoms of acute SCI include paralysis, numbness, or loss of bladder or bowel control. Despite investigations into potential neuroprotective and regenerative therapies, treatment options for patients with acute SCI remain scarce [1]. Currently, targeted blood pressure management, methylprednisolone or spinal cord decompression are commonly used clinically. The timing of surgical decompression is important. Although the optimal timing remains controversial, spinal decompression, vertebra stabilization, and maintenance of blood perfusion have been recognized as key factors for optimal outcomes in traumatic SCI [2]. A recent systematic review and meta-analysis by Qiu et al. [3] to compare the efficacy of early and late surgical interventions for acute SCI was published in a recent issue of International Journal of Surgery. The authors came to the conclusion that “compared with late surgery, acute SCI patients who underwent early surgery experienced greater recovery after spinal injury, with better neurological improvement, shorter length of stay, less charges and lower incidence of complications.” Although these results are of great importance, we would like to underline some issues on identification of included studies, quality assessment, and data analysis that are important in interpreting the findings of this study. First, detailed registration information should be highlighted and explained in the article. Registering an systematic review protocol is important as it enables promotion of transparency and avoidance of potential biases including both selection and selective outcome reporting biases. Second, it is not enough that only four databases were searched. Other English databases such as PsycINFO, Google Scholar, NLM Gateway, and BIOSIS previews should also be searched. Third, the authors clearly mentioned in the Methods that “two assessors receiving normative training beforehand independently evaluated the quality of all the included studies using the 9-star Newcastle-Ottawa Scale (NOS)”. However, some scholars believe that the NOS score has unknown validity at best, or it can include quality items that are even invalid. Using this score in evidence-based reviews and meta-analysis may produce highly arbitrary results [4]. Therefore, we suggest a modified version of the Downs and Black tool be used to assess the methodological quality of the non-randomized cohort studies [5]. In addition, the Kappa score, which measures agreement between reviewers, should also be provided in the article. Fourth, the authors used an inverse variance random effects model to pool the data in this review. In our opinion, these studies should be combined by using the DerSimonian and Laird random effects model, which considers both within- and between-study variations. Finally, although the funnel plot is the most common method used in detecting publication bias, as a rule of thumb, tests for funnel plot asymmetry should be used only when there are at least 10 studies included in the meta-analysis. It is unwise to use this method here because when there are fewer studies, the power of the test is too low to distinguish chance from real asymmetry. The Egger’s bias test is more appropriate to be used in this article. We respectfully appreciate that Qiu et al. provided us with an important meta-analysis which provides a guide for clinical decision-making. However, more studies with large sample sizes and good scientific designs are required on this topic. Provenance and peer review Commentary, internally reviewed. Funding None. Ethical approval Not Applicable. Research registration unique identifying number (UIN) Not applicable. Author contribution Liyun Jiang and Jin Zhang conceived, designed, and planed the study. Liyun Jiang supervised the study. Teng Chen drafted the manuscript. Zongping Xiao critically revised the manuscript for important intellectual content. All authors have full access to the manuscript and take responsibility for the study design. All authors have approved the manuscript and agree with submission. Guarantor Liyun Jiang. Declaration of competing interest None.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Méta-épidémiologie (sens large)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0120,010
Bibliométrie0,0030,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,294
Tête enseignante GPT0,533
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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