Support for a Common Metric for Pediatric Pain Intensity Scales
Bibliographic record
Abstract
Institutional adoption of routine measurement of pediatric pain has been impeded partly by the profusion of different metrics (eg, 0 to 5, 0 to 6, 0 to 10, 0 to 100) for reporting pain intensity on various instruments. The present paper discusses the importance of adopting a common metric, that is, a single numbering system on which estimates of pain intensity from various sources can be recorded. To explore both support and reservations concerning the adoption of a common metric, a survey questionnaire was sent in 1999 to an estimated 600 subscribers to the Pediatric Pain Internet Mailing List. Individuals working in pediatric institutions where children′s pain is routinely measured, or where adoption of such measures is planned, were requested to respond by e‐mail or mail. Responses (n=37) were from nurses (49%), physicians (24%), psychologists (7%) and others/unlisted (20%) on four continents. Adoption of a common metric was endorsed by 81% of respondents. Among the possible numbering systems, the 0 to 10 system was strongly favoured (70%) over other options. Respondents commented that adoption of a common metric would improve communication and consistency in measurement both within and among institutions. Some disadvantages, such as staff resistance to altering existing systems, were also suggested. The majority of respondents thought that it would be desirable to adopt a common metric. Among the possible numbering systems, the 0 to 10 system is by far the most favoured. Adopting a common 0 to 10 standard, and adapting existing tools to that metric, would be positive steps toward identifying and relieving children′s pain.
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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 | 0.209 | 0.510 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".