Domains of Chronic Low Back Pain and Assessing Treatment Effectiveness: A Clinical Perspective
Bibliographic record
Abstract
Nonspecific chronic low back pain (CLBP) is a common clinical condition that has impacts at both the individual and societal level. Pain intensity is a primary outcome used in clinical practice to quantify the severity of CLBP and the efficacy of its treatment; however, pain is a subjective experience that is impacted by a multitude of factors. Moreover, differences in effect sizes for pain intensity are not observed between common conservative treatments, such as spinal manipulative therapy, cognitive behavioral therapy, acupuncture, and exercise training. As pain science evolves, the biopsychosocial model is gaining interest in its application for CLBP management. The aim of this article is to discuss our current scientific understanding of pain and present why additional factors should be considered in conservative CLBP management. In addition to pain intensity, we recommend that clinicians should consider assessing the multidimensional nature of CLBP by including physical (disability, muscular strength and endurance, performance in activities of daily living, and body composition), psychological (kinesiophobia, fear-avoidance, pain catastrophizing, pain self-efficacy, depression, anxiety, and sleep quality), social (social functioning and work absenteeism), and health-related quality-of-life measures, depending on what is deemed relevant for each individual. This review also provides practical recommendations to clinicians for the assessment of outcomes beyond pain intensity, including information on how large a change must be for it to be considered "real" in an individual patient. This information can guide treatment selection when working with an individual with CLBP.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".