Pain Prevalence in Nine‐ to 13‐Year‐Old School Children
Bibliographic record
Abstract
BACKGROUND: Despite significant progress in the epidemiology of chronic pain in adults, major gaps remain in our understanding of the epidemiology of chronic pain in children. In particular, the incidence, prevalence and sensory characteristics of many types of pain in Canadian children are unknown. OBJECTIVES: A study to obtain the lifetime and point prevalence of common acute pains, recurrent pain syndromes and chronic pains was conducted in a cohort of 495 school children, nine to 13 years of age, in eastern Ontario. METHODS: Children reported their pain experiences and described the intensity, affect and duration of the pains experienced over the previous month by completing the Pain Experience Interview -- Short Form. RESULTS: The majority of children (96%) experienced some acute pain over the previous month, with headache (78%) being most frequently reported. Lifetime prevalence for certain acute pains differed significantly by sex (P<0.05). Fifty-seven per cent of children reported experiencing at least one recurrent pain, while 6% were identified as having had or currently having chronic pain. DISCUSSION: The prevalence of acute pain in this Canadian cohort is consistent with international estimates of acute pain experiences (ie, headache) and recurrent pain problems (ie, recurring headache, abdominal pain and growing pains). However, 6% of children reported chronic pain. The self-completed Pain Experience Interview--Short Form provides a feasible administration technique for obtaining population estimates of childhood pain, and for conducting longitudinal studies to identify risk and prognostic factors for chronic pain.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".