Evaluating Common Outcomes for Measuring Treatment Success for Chronic Low Back Pain
Notice bibliographique
Résumé
STUDY DESIGN: Systematic review. OBJECTIVE: To identify, describe, and evaluate common outcome measures in patients with chronic low back pain (CLBP). SUMMARY OF BACKGROUND DATA: The treatment of CLBP has been associated with multiple clinical challenges. Further complicating this is the myriad of outcome scores used to assess treatment of CLBP. These scores have been used to examine different domains of patient satisfaction and quality of life in the literature. Critical assessment of the frequency, parity, and the quality of these outcomes are essential to improve our understanding of CLBP. METHODS: A systematic review of the English-language literature was undertaken for articles published from January 2001 through December 31, 2010. Electronic databases and reference lists of key articles were searched to identify measures used to evaluate outcomes in six different domains in patients with CLBP. The titles and abstracts of the peer-reviewed literature of LBP were searched to determine which of these measures were most commonly reported in the literature and which have been validated in populations with CLBP. RESULTS: We identified 75 outcome measures cited to evaluate CLBP. Twenty-nine of these outcome measures were excluded because of only a single citation leaving 46 measures for the evaluation. The most commonly used functional outcomes were the Oswestry Disability Index, Roland Morris Disability Index, and range of motion. For pain, the Numeric Pain Rating Scale, Brief Pain Inventory, Pain Disability Index, McGill Pain Questionnaire, and visual analog scale were most commonly cited. For psychosocial function, the Fear Avoidance Beliefs Questionnaire, Tampa Scale for Kinesiophobia, and Beck Depression Inventory were most commonly used. For generic quality of life, short form 36, Nottingham Health Profile, short form 12, and Sickness Impact Profile were the most common measures. For objective measures, the work status/return to work, complications or adverse events, and medications used were the most commonly cited. For preference-based measures, the Euro-Quol 5 dimensions and short form 6 dimensions were most commonly cited. The validity, reliability, responsiveness, universality, and potential proprietary requirements are summarized for each. CONCLUSION: Outcome measures should be routinely assessed in patients with CLBP. The choice of appropriate outcome measure should be influenced by the study objectives and design, as well as properties of the particular measure within the context of CLBP. CLINICAL RECOMMENDATIONS: Recommendation 1: When selecting the appropriate outcome measures for clinical or research purposes, consider domains that best measure what are most important to patients. Measures that are valid, reliable, and responsive to change should be considered first. Other considerations include the number of items required (especially in the context of multiple measures), whether the measure is validated in the relevant language, and the associated costs or fees. Strength: Strong Recommendation 2: Domains of greatest importance include pain, function, and quality of life. If cost utilization is a priority, then preference-based measures should be considered. For pain, we recommend the VAS and NRPS because of their ease of administration and responsiveness. For function, we recommend the ODI and RMDQ. The SF-36 and its shorter versions are most commonly used and should be considered if quality of life is important. If cost utility is important, consider the EQ-5D or SF-6D. Psychosocial tests are best used as screening tools prior to surgery because of their lack of responsiveness. Complications should always be assessed as a standard of clinical practice. Return to work and medication use are complicated outcome measures and not recommended unless the specific study question is focused on these domains. Consider staff and patient burden when prioritizing one's battery of measures.
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 | 0,053 | 0,175 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,016 | 0,016 |
| Bibliométrie | 0,018 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| 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 ».