How Can We Improve Pain Control in Children over the World? Results of International Multiprofessional ICPCN Survey
Bibliographic record
Abstract
Dear Editor: Pain control is the most prominent problem in children's palliative care, especially in developing countries. Many attempts have been made to improve this situation by different organisations including the World Health Organization (WHO) in 2012 (“WHO Guidelines on the Pharmacological Treatment of Persisting Pain in Children with Medical Illnesses.”) While the intent of the WHO guidelines is clear, there are many local country-specified barriers to successful implementation of the recommendations. How can we improve pain control in children all over the world? In order to gain some understanding of different perspectives, the International Children's Palliative Care Network (ICPCN) initiated an International Multiprofessional Survey in 2012. The aim of the survey was to evaluate how ICPCN could help to improve pain control in children around the world. The ICPCN Scientific Committee convened a task force of 25 children's palliative care professionals from 15 countries representing all continents. This task force created a list of eight possible roles the ICPCN could play in improving pain management in children and these roles were described in the survey. Survey participants were …
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".