Pain Outcomes in a US Children’s Hospital: A Prospective Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Pain in hospitalized children may be underrecognized and undertreated. The objective of this survey was to benchmark pain prevalence, intensity, assessment, and pharmacologic as well as integrative treatment of pain in inpatients in a US children's hospital. METHODS: This was a single-day, cross-sectional survey and electronic medical record review of inpatients who received medical care at a pediatric hospital. Inpatients and emergency department patients were asked to report their experience with pain and its management during the previous 24 hours. RESULTS: Of 279 inpatients listed on the morning census, 178 children and parents were located and completed the survey. Seventy-six percent had experienced pain during the previous 24 hours, usually acute or procedural pain, 12% of whom possibly suffered from chronic pain. Twenty percent of all children surveyed experienced moderate and 30% severe pain in that time period. The worst pain reported by patients was caused by needle pokes (40%), followed by trauma/injury (34%). Children and their parents rated 5 integrative, nonpharmacologic modalities as more effective than medications. Pain assessments and management were documented in the medical record for 58% of patients covering the 24-hour period before the morning census. The most commonly prescribed analgesics were acetaminophen, morphine, and ibuprofen. CONCLUSIONS: Despite existing hospital policies and a pain consult team, significant room for improvement in pain management was identified. A hospital-wide, 3-year Lean quality improvement initiative on reducing pain was commenced as a result of this survey.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".