An audit of paediatric pain prevalence, intensity, and treatment at a South African tertiary hospital
Bibliographic record
Abstract
INTRODUCTION: Pain in paediatric inpatients is common, underrecognised, and undertreated in resource-rich countries. Little is known about the status of paediatric pain prevention and treatment in low- and middle-income countries. OBJECTIVES: This audit aimed to describe the prevalence and severity of pain in paediatric patients at a tertiary hospital in South Africa. METHOD: A single-day prospective observational cross-sectional survey and medical chart review of paediatric inpatients at Grey's Hospital, Pietermaritzburg, South Africa. RESULTS: Sixty-three children were included, and mean patient age was 9.7 years (SD 6.17). Most patients (87%) had pain during admission, with 29% reporting preexisting (possibly chronic) pain. At the time of the study, 25% had pain (median pain score 6/10). The worst pain reported was from needle procedures, including blood draws, injections, and venous cannulation (34%), followed by surgery (22%), acute illness/infection (18%), and other procedures (14%). Pharmacological treatments included WHO step 1 (paracetamol and ibuprofen) and step 2 (tramadol, tilidine, and morphine) analgesics. The most effective integrative interventions were distraction, swaddling, and caregiver participation. Although a pain narrative was present in the majority of charts, only 16% had documented pain intensity scores. CONCLUSION: The prevalence of pain in hospitalised children in a large South African Hospital was high and pain assessment inadequately documented. There is an urgent need for pain education and development of guidelines and protocols, to achieve better pain outcomes for children. This audit will be repeated as part of a quality-improvement initiative.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".