Status of weight reduction as an intervention in physical therapy management of low back pain: Systematic review and implications
Bibliographic record
Abstract
Obesity is an independent predictor of back pain and its severity, and healthy weight is associated with less pain and disability, and greater capacity to be active. Given the commitment of physical therapy to health-focused practice, we systematically reviewed current literature on physical therapy management of low back pain with special attention to body weight and its management. Relevant MeSH headings for physical therapy, low back pain and management were used to identify articles in the EMBASE database. The search was limited to randomized controlled trials and published in English over 1 year (June 2011 through May 2012). Of 53 articles meeting criteria, 35 source articles were analyzed. Of these, 17 included initial weight measurement; six included post-intervention weight measurement; five compared weights pre–post-intervention; 18 articles did not include weight as an outcome measure; and none included weight management as either a primary or secondary low back pain intervention. Although the relationship between back pain and overweight has not been established to be causal, this should not exclude its being a focus of contemporary physical therapy practice guidelines. This practice augments patient health consistent with the profession's commitment to the ICF and health-focused practice, and minimizes weight-related contribution to back pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".