The prevalence of pain complaints in a general population in Israel and its implications for utilization of health services.
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of pain complaints, specifically of chronic widespread pain, in the general population; and to explore the utilization of health services by various pain groups. METHODS: Cross sectional population survey of 2210 adults in the southern part of Israel, who were classified into 5 pain groups: no pain, transient pain, chronic regional pain, chronic widespread pain, and other. Participants were interviewed about pain patterns and utilization of health services. RESULTS: Forty-four percent reported pain on the day of the interview. The prevalence of chronic widespread pain in the study population was 9.9%, 14% in women and 3% in men (p<0.01). The prevalence in the Israeli adult population was estimated after adjusting for sex and age as 10.2%. The prevalence of any chronic pain (regional or widespread) increased with age. The prevalence of chronic widespread pain was significantly higher in women than in men across all age groups (p<0.01). Persons with chronic widespread pain reported most frequent visits to their physicians (10.8 visits/year) and most frequent use of antiinflammatory and analgesic drugs. They were more frequently referred to specialists and tended to be hospitalized slightly more often. CONCLUSION: In the general population in Israel, widespread pain is common, and its prevalence is comparable with reports from USA, UK, and Canada.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".