Bibliographic record
Abstract
There have been tremendous research advances in the past 15 years in knowledge about children's pain, and strategies for recognizing and managing that pain. However, the clinical care of children in pain remains a challenge. Children's pain continues to be frequently unrecognized, dismissed or ineffectively managed. A loud call for change is being voiced by physicians, nurses, children and their families. A review of the literature was conducted to document this issue. Starting with a Medline search of the key word 'child* + pain' and continuing with a snowball technique, articles and resources addressing children's pain were collected. Resources presented or published after 1990 were particularly sought because they theoretically reflect both current knowledge about children's pain and the implementation of this knowledge in practice. Unfortunately, although information on pain is available to help children, in many instances, it is not being used. The purpose of the present paper is twofold - to present an overview of current knowledge of children's pain, and factors that hinder its effective assessment and management; and to present a mandate for change. Children's postoperative pain is highlighted in this paper as an example of the gap between pain knowledge and clinical practice. Although treatment strategies differ across different types of pain, children's conditions and ages, the principles and mandate for change discussed in this paper are directly relevant to all categories of children's pain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".