Pain in Hospitalized Children: A Prospective Cross‐Sectional Survey of Pain Prevalence, Intensity, Assessment and Management in a Canadian Pediatric Teaching Hospital
Bibliographic record
Abstract
BACKGROUND: Pain is under-recognised and undertreated. Although standards now exist for pain management, it is not known if this has improved care of hospitalized children. OBJECTIVES: To benchmark pain prevalence, pain intensity, pain assessment documentation and pharmacological treatment of pain. The aim was to highlight areas of good practice, identify areas for improvement and inform development of hospital standards, education, future audits and the research agenda. METHODS: The present prospective cross-sectional survey of all medical and surgical inpatient units took place on a single day at the Hospital for Sick Children (Toronto, Ontario), a Canadian tertiary and quaternary pediatric hospital. A structured, verbally administered questionnaire was used to obtain information on patient demographics, pain before admission, pain intensity during admission and pain treatment. Charts were reviewed to establish frequency of documented pain assessment, the pain assessment tool used and analgesics given. Subgroup analysis was included for age, sex, visible minority or fluency in English, medical versus surgical services and acute pain service input. RESULTS AND CONCLUSIONS: Two hundred forty-one (83%) of the 290 inpatients or their carergivers were interviewed. It was found that 27% of patients usually had pain before admission, and 77% experienced pain during admission. Of these, 23% had moderate or severe pain at interview and 64% had moderate or severe pain sometime in the previous 24 h. Analgesics were largely intermittent and single-agent, although 90% of patients found these helpful. Fifty-eight per cent of those with pain received analgesics in the preceding 24 h but only 25% received regular analgesia. Only 27% of children had any pain score documented in the preceding 24 h. It was concluded that pain was infrequently assessed, yet occurred commonly across all age groups and services and was often moderate or severe. Although effective, analgesic therapy was largely single-agent and intermittent. Widespread dissemination of results to all professional groups has resulted in the development of a continuous quality assurance program for pain at the Hospital for Sick Children. A re-audit is planned to evaluate changes resulting from the new comprehensive pain strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".