Risk Factors for the Development of Low Back Pain in Adolescence
Bibliographic record
Abstract
A previous history and earlier onset of low back pain are associated with chronic low back pain in adults, implying that prevention in adolescence may have a positive impact in adulthood. The study objectives were to determine the incidence of low back pain in a cohort of adolescents and to ascertain risk factors. A cohort of 502 high school students in Montreal, Canada, was evaluated during 1995-1996 at three separate times, 6 months apart. The outcome was low back pain occurrence at a frequency of at least once a week in the previous 6 months. Of the 377 adolescents who did not complain of low back pain at the initial evaluation, 65 developed low back pain over the year (cumulative incidence, 17 percent). Risk factors associated with development of low back pain were high growth (odds ratio = 3.09; 95 percent confidence interval (CI): 1.53, 6.01), smoking (odds ratio = 2.20; 95% CI: 1.38, 3.50), tight quadriceps femoris (odds ratio = 1.02; 95% CI: 1.00, 1.05), tight hamstrings (odds ratio = 1.04; 95% CI: 1.01, 1.06), and working during the school year (odds ratio = 1.33; 95% CI: 1.03, 1.71). Modifying such risk factors as smoking and poor leg flexibility may potentially serve to prevent the development of low back pain in adolescents.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".