Chiropractic management of mechanical neck and low-nack pain: A retrospective, outcome-based analysis
Notice bibliographique
Résumé
BACKGROUND: Evidence suggests that spinal manipulation is an effective treatment for mechanical neck and low-back pain (LBP). Treatment efficacy is important to establish for these symptoms because combined they account for a considerable amount of disability and substantial associated direct and indirect costs to society. OBJECTIVE: The purpose of this study was to examine the outcome of patients undergoing chiropractic treatment for mechanical neck or LBP. DESIGN AND SETTING: A retrospective, outcome-based analysis was done for patients seeking care at a private chiropractic practice over a 1-year period. A total of 512 files were reviewed, with 119 patients selected for inclusion. Patients were included if their chief symptom was uncomplicated mechanical neck or LBP. Diagnoses included cervical, lumbar, or sacroiliac joint sprain/strain (International Code of Diagnostics version 9 [ICD-9] code: 847.1, 847.3, 846.1, respectively), discogenic LBP (ICD-9: 722.1), and headaches (ICD-9: 784.0) because many patients with neck pain presented with concomitant headaches. Disability and pain were measured with the modified Oswestry scale (for the patients with LBP), Neck Disability Index, and an 11-box visual analogue pain scale before and after treatment. Treatment consisted of spinal manipulation, various soft-tissue techniques, home-care instructions, and ergonomic and return-to-activity advice, including rehabilitative exercises. Patients received an average of 12 treatments over a 4-week period. Statistical analysis was performed on pretreatment and posttreatment values for both disability and pain. Stratification was based on duration (acute/subacute, chronic, acute exacerbation of a chronic condition) and severity (mild, moderate, or severe) of symptoms. RESULTS: Statistically significant reductions in disability and pain scores were achieved in all groups. An average 52.5% and 52.9% reduction in pain and disability, respectively, was achieved in the low-back group. The chronic LBP group realized a less statistically significant reduction of pain and disability (19.7% and 19.8%, respectively) than the acute/subacute (66.8% and 62.5%) or the chronic/recurrent group (56. 5% and 63.4%). The differences were statistically significant. Patients with neck pain had an average 53.8% and 48.4% reduction in their pain and disability, respectively. Patients with concomitant neck pain and headaches had statistically significant higher pretreatment and posttreatment disability and pain scores than those with only neck pain. There was no statistically significant difference in outcomes between groups stratified according to pain intensity. CONCLUSIONS: Patients attending a private chiropractic clinic for treatment of mechanical neck pain or LBP had statistically significant reductions in their pain-related disability after treatment. These results indicate that chiropractic manipulation is beneficial for the treatment of mechanical neck pain and LBP. However, care must be taken when drawing conclusions from these outcomes. The study design does not account for the natural history of low back- or neck pain-related disability and therefore does not allow for claims of treatment efficacy. In addition, it has been suggested that patients presenting to medical doctors with these symptoms have significant overlying comorbidity when compared with patients presenting to a chiropractor.
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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».