Notice bibliographique
Résumé
Commentary Complex periarticular knee trauma presents many challenges for both the patient and the physician. One of the most debilitating postoperative sequelae is knee stiffness. Although that complication is rare after elective knee surgery, Gaston et al.1 reported that 20% of the patients in their prospective study had stiffness (a residual knee flexion contracture of >5°) at twelve months after surgery for a tibial plateau fracture. Range of motion after both elective and emergency knee surgery has been shown in multiple studies to correlate with functional outcome. Although Miner et al.2 reported that knee flexion after total knee arthroplasty did not correlate directly with outcome, their findings did reveal a significant decrease in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for patients with <95° of flexion. The WOMAC scores at twelve months postoperatively correlated with patient satisfaction and perceived improvement. In 2008, Ritter et al.3 studied the knee range of motion of patients after posterior cruciate-retaining arthroplasty and reported substantially worse outcomes in patients with <118° of total motion. Although multiple factors have been identified as increasing the risk of stiffness following total knee arthroplasty4 as well as after sports medicine reconstruction5, there have been no previous significant studies examining the predictors for knee stiffness after treatment of knee fracture. The authors of the current case-control study attempted to describe these factors by examining patients requiring knee manipulation after periarticular knee fracture. Although it is not an exact measure of postinjury stiffness, knee manipulation was chosen by the authors as the identifying marker of knee stiffness. This benchmark clearly does not capture all patients with potentially function-limiting knee stiffness; however, its use as the defining element of knee stiffness, as opposed to trying to define a specific critical range of motion, allows a relatively clear delineation between patient groups to allow for comparison. Despite a relatively small number of patients, the authors matched these patients with a control group with similar ages and fracture severity (as measured with the AO/OTA classification). The authors are to be commended for this effort and their success in carrying it out. Although patients treated with manipulation had a significant improvement in their range of motion compared with that before the manipulation, the final results revealed a significant difference between the final flexion of the case group compared with the control group (107° versus 124°). The factors associated with increased knee stiffness included extensor mechanism disruption, a need for fasciotomy, ongoing wound management, and three or more surgical procedures on the limb. Multiple factors found to have no impact on outcome included age, sex, side of injury, mechanism of injury, length of intensive care unit stay, head injury, use of continuous passive motion, and discharge to home versus to a rehabilitation facility. Finally the authors were able to conclude that late manipulation of a stiff knee after fracture is a worthwhile procedure, improving the range of motion by an average of 62°. As do all studies, this one had limitations whose impact must be recognized. The retrospective chart review limited the ability to verify the accuracy of documented knee motion measurements. The exact timing and nature of physical therapy compliance, which could have affected the results, could not be measured. Finally, there was some overlap between the category of open wound management and presence of fasciotomy that leads to difficulty in identifying whether each factor individually predicts knee stiffness (or both do together). Despite these limitations, this study definitely advances our knowledge in an area of orthopaedics in which there is a dearth of published knowledge. Simply put, this study will be very helpful to all orthopaedic surgeons who treat trauma patients. Clinically, this study has the potential to change how we view and treat periarticular knee injuries. Just as studies examining immediate intramedullary nailing of open tibial fractures ultimately led to a paradigm shift in orthopaedic trauma twenty years ago, this study may be the first step in changing how we manage these periarticular fractures in the future. A focus on closing wounds sooner, obtaining definitive fracture fixation sooner, and limiting the overall number of surgical interventions might improve patient outcomes after these very difficult injuries. Earlier identification of “at risk patients” could also lead to a decision to return to the operating room for manipulation and quadricepsplasty procedures earlier in the patient’s rehabilitation course. Furthermore, the study’s findings will enable orthopaedic surgeons to better counsel patients and their families with regard to expected outcomes. This additional knowledge should help most patients adjust more readily to their “new reality” as they proceed through the long and difficult rehabilitation process after major orthopaedic knee trauma.
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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,008 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,005 |
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 ».