A Delphi Study to Identify Indicators of Poorly Managed Pain for Pediatric Postoperative and Procedural Pain
Bibliographic record
Abstract
BACKGROUND: Adverse health care events are injuries occurring as a result of patient care. Significant acute pain is often caused by medical and surgical procedures in children, and it has been argued that undermanaged pain should be considered to be an adverse event. Indicators are often used to identify other potential adverse events. There are currently no validated indicators for undertreated pediatric pain. OBJECTIVES: To develop a preliminary list of indicators of undermanaged pain in hospitalized pediatric patients. METHODS: The Delphi technique was used to survey experts in pediatric pain management and quality improvement. The first round used an electronic questionnaire to ask: "In your opinion, what indicators would signify that acute pain in a child has not been adequately controlled?" Responses were grouped together in semantically similar themes, providing a list of possible adverse event indicators. Using this list, an electronic questionnaire was developed for round 2 asking respondents to indicate the importance of each potential indicator. RESULTS: All but one indicator achieved a level of consensus ≥70%. Separate indicators emerged for postoperative and procedural pain. An additional distinction was made between indicators that could be identified by chart review and those requiring observation of practice and assessment from the child or parent. DISCUSSION: The adverse care indicators developed in the present study require further refinement. There is a need to test their clinical usability and to determine whether these indicators actually identify undermanaged pain in clinical practice. The present study is an important first step in identifying undermanaged pain in hospital and treating it as an adverse event. CONCLUSION: The adverse care indicators developed in the present study are the first step in conceptualizing mismanaged pain as an adverse event.
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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 | 0.058 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".