Baseline individual factors associated with clinical outcomes in adults with non-specific low back pain following manual therapy: a systematic review
Notice bibliographique
Résumé
BACKGROUND: Primary care providers consider the identification of patient subgroups as a high research priority. Unfortunately, evidence to support the benefit of treatments targeting subgroups of patients with NSLBP remains inconsistent. Specifically, little is known about baseline individual patient characteristics associated with optimal clinical improvement from manual therapy. This systematic review aims to identify baseline individual factors (BIFs), including patient characteristics, self-reported questionnaires, clinical examination, and ancillary test factors associated with clinical improvement (or lack of) among adult patients with Non-Specific Low Back Pain (NSLBP) following manual therapy. METHODS: A systematic review of published evidence in Medline, Embase, Cochrane, Index To Chiropractic Literature, and CINAHL was conducted until April 2024. Studies included participants aged 18 years and over with NSLBP and without radiculopathy. Participants received manual therapies, including musculoskeletal manipulation/mobilization (spinal and extremities) and soft tissue therapy. We excluded mechanically assisted manipulations and interventions mainly involving exercise, education, and/or advice. Two independent assessors screened studies for inclusion, extracted data, and assessed risks of bias using the Quality In Prognosis Studies (QUIPS) Tools. A qualitative synthesis of findings was undertaken. BIFs were synthesized according to patient-reported outcomes measure domains: 1) pain intensity measures, 2) disability measures, 3) global perceived effect, and 4) other factors (e.g., satisfaction with care, total number of visits). RESULTS: Data from 19 studies (reported in 21 articles) involving 4,689 participants were analyzed. Twelve studies reported pain intensity, 18 reported disability outcomes, and 4 reported patient's global perceived effect. Over 70% of the included studies had a high risk of confounding bias. Included studies explored the potential association between clinical outcomes and 172 BIFs. BIFs were categorized into patient characteristics (n = 40), self-reported questionnaire (n = 31), clinical examination (n = 82), and ancillary tests (n = 20). Fourteen multivariate models explored the association with clinical improvement, and four others investigated the association with non-improvement. Findings were inconsistent across studies. CONCLUSION: Using BIFs in clinical practice to predict clinical outcomes following manual therapy treatment appears to be premature. Future studies should aim to replicate the results and differentiate prognostic factors from treatment effect modifiers. TRIAL REGISTRATION: CRD42019131416.
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 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 | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».