KNEE REPLACEMENT RISK PREDICTION MODELING fOR KNEE OSTEOARTHRITIS USING CLINICAL AND MAGNETIC RESONANCE IMAGE FEATURES: DATA FROM THE OSTEOARTHRITIS INITIATIVE
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
This study aims to develop effective predictive models to assess knee replacement (KR) risk in knee osteoarthritis (KOA) patients, which is important in the personalized diagnosis, assessment, and treatment of KOA. A total of 269[Formula: see text]KOA patients were selected from the osteoarthritis initiative (OAI) public database and their clinical and knee cartilage image feature data were included in this study. First, the clinical risk factors were screened using univariate Cox regression and then used in the construction of the Clinical model. Next, their image features were selected using univariate and least absolute shrinkage and selection operator (LASSO) Cox methods step by step, and then used in the construction of the Image model. Finally, the Image+Clinical model was constructed by combining the Image model and clinical risk factors, which was then converted into a nomogram for better visualization and future clinical use. All models were validated and compared using the metric of C-index. In addition, Kaplan–Meier (KM) survival curve with log-rank test and calibration curve were also included in the assessment of the model risk stratification ability and prediction consistency. Age and three Western Ontario and McMaster Universities (WOMAC) scores were found significantly correlated with KR, and thus included in Clinical model construction. Fifty-eight features were selected from 92[Formula: see text]knee cartilage image features using univariate cox, and four image features were retained using the LASSO Cox method. Image+Clinical model and nomogram were finally constructed by combining clinical risk factors and the Image model. Among all models, the Image+Clinical model showed the best predictive performance, and the Image model was better than the Clinical model in the KR risk predictive consistency. By determining an optimal cutoff value, both Image and Image+Clinical models could effectively stratify the KOA patients into KR high-risk and low-risk groups (log-rank test: [Formula: see text]). In addition, the calibration curves also showed that model predictions were in excellent agreement with the actual observations for both 3-year and 6-year KR risk probabilities, both in training and test sets. The constructed model and nomogram showed excellent risk stratification and prediction ability, which can be used as a useful tool to evaluate the progress and prognosis of KOA patients individually, and guide the clinical decision-making of KOA treatment and prognosis.
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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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 découle