Cross-sectional study of pediatric pain prevalence, assessment, and treatment at a Canadian tertiary hospital
Bibliographic record
Abstract
Background Painful experiences are common among hospitalized children. Long-term negative biopsychosocial consequences of undertreated pain are recognized.Aims The study benchmarks pain prevalence, assessment, and treatment as first steps to improve pain care in a Canadian tertiary hospital.Methods Single-day audits were undertaken on the pediatric ward (PW), pediatric emergency department (ED), and maternal services (MS). Participants (child or caregiver proxy) reported hospital pain experiences in the preceding 24 h; medical records were reviewed for assessment and treatment.Results Among 84 participants, pain prevalence ranged from 75% to 88%; mean pain intensity ranged from 5.7 to 6.5/10. Prevalence of moderate to severe pain was 78% on PW, 65% in ED, and 55% on MS; needle pokes were the most frequent cause of worst pain. Documentation of pain assessment varied by setting (PW, 93%; ED, 13%; MS, 0%). Documented maximum pain scores were significantly lower compared to participant report (mean difference 4.5/10, SD 3.1, P < 0.0001). A total 29% (6/21) of infants with heel lance or injection received breastfeeding or sucrose, and 29% (7/24) of participants receiving other needle procedures had documented or reported topical lidocaine use. All participants on MS underwent needle procedures.Conclusions Pain is experienced commonly by infants and children in PW, ED, and MS. Pain assessment documentation is not routine and underestimates participant report. Evidence-based pain management strategies are underutilized. An institution-wide quality improvement approach is required to address pain care. Pain assessment and needle pain prevention and treatment should be prioritized in these pediatric acute care and newborn care settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".