Chiropractic management of mechanical neck and low-nack pain: A retrospective, outcome-based analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".