The Construction and Implementation of a Novel Postburn Pruritus Scale for Infants and Children Aged Five Years or Less
Bibliographic record
Abstract
The authors' objectives were to design, refine, validate and implement a behavior-anchored postburn pruritus scale for children aged 5 years or less. We engaged a range of professionals involved in the care of children with burns. We used Q-methodology in interprofessional team exercises to identify and stratify itch behaviors into categories of increasing severity, and then iteratively refined these into a draft scale. We used a range of quantitative and qualitative techniques to assess the utility, feasibility, and validity of the scale and refined it accordingly. During the implementation phase we collected some preliminary reliability data. We generated a 4-point scale of itch severity with simple descriptors of each score. We also designed a separate guidance note and example behaviors that could be used to orientate new users without the need for rater training. End-user interviews revealed high levels of feasibility and content validity. The reliability data showed moderate inter-observer agreement, with a Cohen's kappa of 0.52 (P < .001). We have developed and implemented a behavioral post-burn pruritus scale for use in children aged less than 5 years and have demonstrated its utility, feasibility, validity, and reliability. The development of a validated symptom scoring scales will allow for the conduct of high-quality quantitative clinical trials and the subsequent implementation of evidence-based management protocols.
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 | 0.030 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".