A mixed treatment comparison of selected osteopathic techniques used to treat acute nonspecific low back pain: a proof of concept and plan for further research
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Résumé
CONTEXT: Back injuries have a high prevalence in the United States and can be costly for both patients and the healthcare system at large. While previous guidelines from the American College of Physicians for the management of acute nonspecific low back pain (ANLBP) have encouraged nonpharmacologic management, those treatment recommendations involved only superficial heat, massage, acupuncture, and spinal manipulation. Investigation about the efficacy of spinal manipulation in the management of ANLBP is warranted. OBJECTIVES: To compare the results in previously-published literature documenting the outcomes of osteopathic manipulative treatment (OMT) techniques used to treat ANLBP. The secondary objective of this study was to demonstrate the utility of using Bayesian network meta-analysis (NMA) to perform a mixed treatment comparison (MTC) of a variety of osteopathic techniques. METHODS: A literature search for randomized controlled trials (RCTs) of ANLBP treatments was performed in April 2020 according to PRISMA guidelines by searching MEDLINE/PubMed, OVID, Cochrane Central, PEDro, and OSTMED.Dr databases; scanning the reference lists of articles; and using the Canadian Agency for Drugs and Technologies in Health grey literature checklist. Each database was searched from inception to April 1, 2020. The following search terms were used: acute low back pain, acute low back pain plus physical therapy, acute low back pain plus spinal manipulation, and acute low back pain plus osteopathic manipulation. The validity of eligible trials was assessed by the single author using an adapted National Institute for Health and Care Excellence methodology checklist for randomized, controlled trials and an extraction form based on that checklist. The outcome measure chosen for this NMA was the Visual Analogue Scale of pain. The NMA were performed using the GeMTC user interface for automated NMA utilizing a Bayesian hierarchical model of random effects. RESULTS: The literature search initially found 483 unduplicated records. After screening and full text assessment, five RCTs were eligible for the MTC, yielding a total of 430 participants. Results of the MTC model suggested that there was no statistically significant decrease in reported pain when exercise, high-velocity low-amplitude (HVLA), counterstrain, muscle energy technique, or a mix of techniques were added to conventional treatment to treat ANLBP. However, the rank probabilities assessment determined that HVLA and the OMT mixed treatment protocol plus conventional care were ranked superior to conventional care alone for improving ANLBP. CONCLUSIONS: While this study failed to provide definitive evidence upon which clinical recommendations can be based, it does demonstrate the utility of performing NMA for MTCs of osteopathic modalities used to treat ANLBP. However, to take full advantage of this statistical technique, future studies should be designed with consideration for the methodological shortcomings found in past osteopathic research.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 découle