Bibliographic record
Abstract
BACKGROUND: Neck and low back pain are leading causes of morbidity and health care utilization. However, little is known about the characteristics that differentiate those who seek from those who do not seek health care for their pain. OBJECTIVES: The objectives of this study were to: 1) describe health care utilization for neck and back pain; 2) determine the characteristics of individuals seeking health care for neck and back pain; and 3) identify the characteristics of patients who consult medical doctors, chiropractors, or both. DESIGN: Population-based cross-sectional mailed survey. SUBJECTS: Subjects were randomly selected adults from the Saskatchewan Health Insurance and Registration File. MEASURES: Demographic, socio-economic, general health, comorbidity, health-related-quality-of-life, pain severity and health care utilization data were collected. The main outcome was whether subjects with prevalent neck or low back pain visited a health care provider in the previous month. RESULTS: Twenty-five percent of individuals with neck or low back pain visited a health care provider. Seeking health care was associated with disabling neck or back pain, digestive disorders, worse bodily pain and worse physical-role-functioning. Compared with medical patients, fewer chiropractic patients lived in rural areas or reported arthritis, but they reported better social and physical functioning. More patients consulting both providers reported disabling neck or back pain. CONCLUSIONS: Individuals seeking care for neck or back pain have worse health status than those who do not seek care. Patients consulting chiropractors alone report fewer comorbidities and are less limited in their activities than those consulting medical doctors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".