Physical measures of physical functioning as prognostic factors to predict outcomes in low back pain: A systematic review and narrative synthesis
Notice bibliographique
Résumé
BACKGROUND: Low back pain (LBP) remains a major global health challenge. Effective management of LBP requires prognostic research to identify people at risk of poor outcome, enabling timely and targeted interventions. OBJECTIVE: To synthesize the evidence for physical measures of physical functioning as prognostic factors for predicting outcome in LBP. METHODS: This systematic review followed PRISMA and published protocol [PROSPERO-CRD42023406796] [1]. Searches were conducted in MEDLINE, EMBASE, CINAHL, Scopus and ProQuest Dissertations/Theses from inception to 29/5/2024. Hand searches of key journals and screening reference lists of included studies was performed. Prospective longitudinal studies, evaluating physical measures of physical functioning as prognostic factors, in adults 18years≥ with LBP and/or LBP-related leg pain were included. LBP related to malignancy, fracture, infection, cauda equina, inflammatory conditions, and measures; imaging, EMG, and motion capture with force plates or 3D video analysis were excluded. Two independent reviewers screened articles, extracted data, assessed risk of bias (RoB) using QUIPS. Due to high heterogeneity a narrative synthesis was conducted and GRADE determined the quality of evidence. RESULTS: From 15,889 citations, 42 studies were included, with 50% assessed as high RoB. Low-quality evidence supports no predictive ability of high isometric back extension endurance, high handgrip strength, and high fingertip-to-floor test for good long term LBP outcomes. Very low-quality evidence supports inconsistent predictive ability of high lumbar extension range of motion and high straight leg raise range for good short-term outcomes, and high isometric back flexion endurance for good long-term LBP outcome. For studies that could not be synthesized, 41 physical measures of physical functioning were investigated, with 23 of them showing promising predictive ability for LBP outcome. CONCLUSION: This review highlights a lack of high-quality evidence regarding the predictive ability of physical measures of physical functioning in LBP. Findings indicate that the existing evidence is low-quality for no predictive ability and very low-quality for inconsistent predictive ability of physical measures of physical functioning. Low/very low-quality evidence suggests cautious interpretation. Imprecision, high RoB studies, and inadequately controlled confounding factors contributed to low/very low-quality evidence. This review also identifies emerging potential prognostic factors. An adequately powered, low RoB prospective longitudinal study using standardized measurement protocols and multivariable analysis is required to further investigate the promising predictive ability of physical measures of physical functioning in LBP. Future prognostic research should be grounded in strong theoretical rationale, including biological plausibility.
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 | 0,036 | 0,124 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,013 | 0,012 |
| Bibliométrie | 0,016 | 0,013 |
| 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) | 0,006 | 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 ».