Early Versus Delayed Surgery Results In Similar Outcomes Following Multiligament Knee Injury; A Prospective Cohort
Notice bibliographique
Résumé
Objectives: Multiligament knee injuries (MLKI’s) are rare, but life-altering injuries that have significant implications on patients’ functional status, quality of life and return to work and sport. While the treatment of these injuries is far from standardized, there exists good evidence to support operative intervention. Unfortunately there are no guidelines to indicate the ideal timing of surgery after injury to optimize clinical outcomes and patient satisfaction. With that in mind, the aim of this study was to assess injury specific functional outcomes following surgery for MKLIs and identify advantages to early versus delayed surgery. Methods: Between 2006-2017, adults with MLKIs were identified at an academic level 1-trauma center. Patients were enrolled in prospective data collection and followed routinely after surgery. Study participants were stratified into early (<45 days from injury) and delayed surgical intervention (>45 days from injury). The primary analysis was patient reported outcomes in the form of a specific and validated score (MLQOL) to compare early vs delayed surgical intervention. We secondarily analyzed associations between age, gender, BMI, injury severity (Schenck classification), associated nerve injuries, and reoperation rates to our primary outcome measure. Results: A total of 152 patients were identified, with 85 patients in the early group and 67 in the delayed. The mean time to surgery was 16.7 ± 7.9 days in the early group versus 253 ± 132.6 days in the delayed. Average follow-up was 46.7 months. There was no significant difference between groups with respect to age, gender distribution, BMI, injury mechanism, nor severity. The early surgery group was found to include more patients with lateral sided injuries (p=0.010) as well as nerve injuries at initial presentation (p=0.002). The delayed group was found to have more patients with PCL involvement compared to early surgery (P=0.003). We did not identify a significant difference in pain, stiffness, or instability patient reported outcome scores between the early and delayed surgical groups. 49 of the 152 patients underwent a repeat operation (32%) including manipulation under anesthesia, arthroscopic debridement, removal of hardware, revision and/or conversion to total knee arthroplasty. No significant difference was found in reoperation rates between the two groups. In our primary outcome, we did not identify any relationship between early or late surgery and MLQOL scores. In our secondary analysis, using a linear regression model, we determined older age to be independently predictive of poor outcomes with respect to pain (p=0.018), stiffness (p=0.048) and instability (p=0.004) as assessed through MLQOL questionnaire. Conclusion: In our analysis, stratifying patients to early vs delayed surgery had no effect patient reported outcomes following MLKI reconstruction. In the secondary outcome measures, we identified that regardless of time from injury to surgical intervention, older age was independently predictive of poor pain, stiffness, and instability scores in the MLQOL questionnaire. To our knowledge this is the largest prospectively collected dataset of injury specific outcomes in MLKIs. This analysis will allow for an evidence based approach to guide discussion, manage expectations, and predict outcomes of MLKI patients as well as provide the framework for how surgeons manage these significant injuries.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».