improving Pain mAnagement for childreN and young people attendeD by Ambulance (PANDA): protocol for a realist review.
Bibliographic record
Abstract
Background: Each year in England, 450,000 children and young people (CYP) under 18 years of age are transported by ambulance to emergency departments. Approximately 20% of these suffer acute pain caused by illness or injury. Pain is a highly complex sensory and emotional experience. The intersection between acute pain, unwell CYP and the unpredictable pre-hospital environment is convoluted. Studies have shown that prehospital pain management in CYP is poor, with 61% of those suffering acute pain not achieving effective pain relief (abolition or reduction of pain score by 2 or more out of 10) when attended by ambulance. Consequences of poor acute pain management include altered pain perception, post-traumatic stress disorder and the development of chronic pain. This realist review will aim to understand how ambulance clinicians can provide improved prehospital acute pain management for CYP. Methods: A realist review will be conducted in accordance with the Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) guidance. A five-stage approach will be adopted; 1) Developing an Initial Programme Theory (IPT): develop an IPT with key stakeholder input and evidence from informal searching; 2) Searching and screening: conduct a thorough search of relevant research databases and other literature sources and perform screening in duplicate; 3) Relevance and rigour assessment: assess documents for relevance and rigour in duplicate; 4) Extracting and organising data: code relevant data into conceptual "buckets" using qualitative data analysis software; and 5) Synthesis and Programme Theory (PT) refinement: utilise a realist logic of analysis to generate context-mechanism-outcome configurations (CMOCs) within and across conceptual "buckets", test and refine the IPT into a realist PT. Conclusion: The realist PT will enhance our understanding of what works best to improve acute prehospital pain management in CYP, which will then be tested and refined within a realist evaluation. Registration: PROSPERO Registration: CRD42024505978.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.120 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.082 | 0.015 |
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".