Treatment of Chronic Low-Back Pain: A 1-Year or Greater Follow-Up
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal disorders are the most frequent cause of physical disability in developed countries. In particular, lower back pain is very common and represents a significant burden both in terms of patient suffering and costs. It is important to determine the most effective and efficient interventions in the treatment of this condition. PURPOSE: This article presents clinical outcomes in patients with chronic lower back pain involving ligamentous pathology treated by a physician who specializes in injection therapy for chronic musculoskeletal conditions and is able to tailor injection therapy specifically to each individual patient. DESIGN: As a retrospective case series, patients from outpatient clinics in both rural and one urban centre were assessed, treated, and had 1 year or more follow-up. METHODS: Patients were referred by their family physician for assessment for chronic lower back pain refractory to conventional medical management. OUTCOME MEASURES: Pain and quality of life (QoL) numeric (10 cm) analog scales (0 = no pain, 10 = the worst pain imaginable; 0 = best quality of life, 10 = worst quality of life) were administered pretreatment and 1 year or later after the last treatment. RESULTS: One hundred and ninety (190) patients were treated during the study period, June 1999-May 2006. Patients whose follow-up was 1 year or greater from the last treatment were included, leaving 140 patients available for data analysis. Both pain and QoL scores were significantly improved at least 1 year after the last treatment. There were no differences in outcomes as a result of age, response to Xylocaine (lidocaine) injection, insurance coverage, smoking history, or gender. CONCLUSIONS: This study suggests that prolotherapy using a variety of proliferants can be an effective treatment for low back pain from presumed ligamentous dysfunction for some patients when performed by a skilled practitioner.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".