Construction and Application of an Informatics Based Multidisciplinary Management Model for Osteoarthritis Patients in Community
Notice bibliographique
Résumé
Background Osteoarthritis has a high rate of disability and deformity, and can be combined with several physical and mental diseases. However, the early symptoms of the disease are not obvious. At present, there are problems in the management of osteoarthritis in the community such as uncoordinated management, inadequate methods and imperfect systems. Objective To construct and evaluate an informatics-based multidisciplinary management model for osteoarthritis patients in community, to promote the management of community osteoarthritis patients and improve the prognosis of the patients. Methods First a multidisciplinary management model of osteoarthritis patients in the community was constructed, including hierarchical management process of patients based on risk factor stratification, the multidisciplinary management team and its division of diagnosis and treatment, then an informatics based multidisciplinary management process was constructed, and information software development was completed. From July 2019 to July 2020, 80 patients with knee osteoarthritis who attended the general outpatient clinics of Dinghai and Daqiao Community Health Service Centers in Shanghai, and the orthopedics outpatient clinics of Yangpu District Central Hospital were randomly assigned into multidisciplinary management groups and general management group, with 40 patients in each group. The patients in general group were given conventional treatment, while the patients in multidisciplinary group were adopted information-based multidisciplinary management. Visual analogue scale (VAS) scores, Western Ontario McMaster University (WOMAC) osteoarthritis index score, the simplified scale of Arthritis Quality Of Life Measurement Scale (AIMS2) scores, Health Literacy Management Scale (HeLMS) scores, and body mass index (BMI) were assessed before and after 12 weeks of management, respectively. Results Before treatment, there were no significant differences in VAS score, WOMAC osteoarthritis index score, AIMS2 score, Helms score, and BMI between patients with knee osteoarthritis in the multidisciplinary and general groups (P>0.05) . After 12 weeks of treatment, the VAS and WOMAC score of both the multidisciplinary and general groups went down, and the health literacy AIMS2 scores and Helms total score were higher after treatment than those before. The difference was statistically significant (P<0.05) . After 12 weeks of treatment, the AIMS2 total score and Helms total score of patients in the multidisciplinary group were higher than those in the general group, and the VAS score, WOMAC osteoarthritis index, and BMI were lower than those in the general group, with significant differences (P<0.05) . Conclusion The implementation of an informatics based community multidisciplinary management model for patients with osteoarthritis of the knee can effectively reduce the patients' joint pain and control their weight, improve their ability of daily living and health literacy, improve the quality of life of patients, and delay the progress of the disease.
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 | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».