Factors Influencing Surgical Decision-Making in the Posterior Laminectomy With Fixation for Degenerative Cervical Myelopathy (POLYFIX-DCM) Trial: Survey Study
Notice bibliographique
Résumé
BACKGROUND: Degenerative cervical myelopathy (DCM) is estimated to affect 2% of the adult population. DCM occurs when degenerative processes cause compression and injure the spinal cord. Surgery to remove the stress caused by the compression of the spinal cord is the mainstay of treatment, with a range of techniques in use. Although various factors are described to inform the selection of these techniques, there needs to be more consensus and limited comparative evidence. OBJECTIVE: The main objective of this survey was to explore the variation of practice and decision-making, with a focus on laminectomy versus laminectomy and fusion in posterior surgery of the cervical spine. We present the results of a survey conducted among the principal investigators (PIs) of the National Institute for Health and Care Research (NIHR) randomized controlled trial on posterior laminectomy with fixation for degenerative cervical myelopathy (POLYFIX-DCM). METHODS: A series of 7 cases were shared with 24 PIs using SurveyMonkey. Each case consisted of a midsagittal T2-weighted magnetic resonance imaging and lateral cervical x-rays in flexion and extension. Surgeons were asked if their preferred approach was anterior or posterior. If posterior, they were asked whether they preferred to instrument and whether they had the equipoise to randomize in the NIHR POLYFIX-DCM trial. Variability in decision-making was then explored using factors reported to inform decision-making, such as alignment, location of compression, number of levels operated, presence of mobile spondylolisthesis, and patient age. RESULTS: The majority of PIs (16/30, 53%) completed the survey. Overall, PIs favored a posterior approach (12/16, 75%) with instrumentation (75/112, average 66%) and would randomize (67/112, average 62%) most cases. Factors reported to inform decision-making poorly explained variability in responses in both univariate testing and with a multivariate model (R2=0.1). Only surgeon experience of more than 5 years and orthopedic specialty training background were significant predictors, both associated with an anterior approach (odds ratio [OR] 1.255; P=.02 and OR 1.344; P=.007, respectively) and fusion for posterior procedures (OR 0.628; P<.001 and OR 1.344; P<.001, respectively). Surgeon experience also significantly affected the openness to randomize, with those with more than 5 years of experience less likely to randomize (OR -0.68; P<.001). CONCLUSIONS: In this representative sample of spine surgeons participating in the POLYFIX-DCM trial as investigators, there is no consensus on surgical strategy, including the role of instrumented fusion following posterior decompression. Overall, this study supports the view that there appears to be a clinical equipoise, and conceptually, a randomized controlled trial appears feasible, which sets the scene for the NIHR POLYFIX-DCM trial.
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 | 0,023 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».