The Role of Classification of Chronic Low Back Pain
Notice bibliographique
Résumé
STUDY DESIGN: Systematic review. OBJECTIVE: To describe the various ways chronic low back pain (CLBP) is classified, to determine if the classification systems are reliable and to assess whether classification-specific interventions have been shown to be effective in treating CLBP. SUMMARY OF BACKGROUND DATA: A classification system by which individual patients with CLBP could be identified and directed to an effective treatment protocol would be beneficial. Those systems that direct treatment have the greatest potential influence on patient outcomes. METHODS: A systematic search was conducted in MEDLINE and the Cochrane Collaboration Library for English language literature published through January 2011. We included articles that specifically described a clinical classification system for CLBP, reported on the reliability of a classification system, or evaluated the effectiveness of classification-specific interventions. RESULTS: A total of 60 articles were initially reviewed. We identified 28 classification systems that met inclusion criteria: 16 diagnostic systems, 7 prognostic systems, and 5 treatment-based systems. In addition, we found 10 randomized controlled trials of CLBP treatment from which we compared inclusion and exclusion criteria. Treatment-based systems were all directed at nonoperative management. Four of the 5 treatment-based systems underwent reliability testing and were found to have interobserver agreement of 70% to 100%. Reliability increased with training and familiarity with a given classification. As the number of subgroups within a classification increased, interobserver agreement decreased. Function and pain were similar between patients treated with the McKenzie classification system and those treated with dynamic strengthening training after 8 months of follow-up in one randomized controlled trial. One prospective cohort study reported better pain and function using the Canadian Back Institute Classification system than with standard rehabilitation. An analysis of the admission criteria to recent randomized studies with either nonoperative care or another surgical intervention provided a methodology for refining criteria to be met by patients considering surgery. CONCLUSION: There currently are many classification systems for CLBP; some that are descriptive, some prognostic, and some that attempt to direct treatment. We recommend that no one classification system be adopted for all purposes. We further recommend that future efforts in developing a classification system focus on one that helps to direct both surgical and nonsurgical treatments. CLINICAL RECOMMENDATIONS: There currently are many classification systems for CLBP; some that are descriptive, some prognostic, and some that attempt to direct treatment. We recommend that no one classification system be adopted for all purposes. We further recommend that future efforts in developing a classification system focus on one that helps to direct both surgical and nonsurgical treatments.
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 | 0,044 | 0,185 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,008 |
| Bibliométrie | 0,012 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».