Validation of a Wound Tool for Assessment of Surgical Wounds in Infants
Bibliographic record
Abstract
BACKGROUND: Wound assessment is a critical part of the care of hospitalized infants in neonatal intensive care. Early recognition and initiation of appropriate treatment of wounds are imperative to facilitate wound healing and avoid complications such as secondary infection and wound dehiscence. There are, however, no validated tools for assessing surgical wounds in infants. PURPOSE: The aim of this study was to develop and interrogate a tool for the assessment of surgical wounds. Specific aims for the tool included interrater reliability (give a consistent and dependable result independent of user) and test criterion validity (give an accurate assessment of the wound compared with an expert). METHODS: This was an exploratory cohort study involving a structured wound tool applied by nursing staff to 40 surgical wounds. The wounds were also assessed by wound experts (a pediatric wound care nurse and a pediatric surgeon). Comparisons were made to elucidate estimates of reliability and validity. RESULTS: The wound tool demonstrated interrater reliability with intraclass correlation coefficient of 0.775 (95% CI, 0.665-0.862) as well as criterion validity with rank correlation coefficient of 0.55 (95% CI, 0.34-0.76) to 0.71 (95% CI, 0.53-0.88). To obtain 100% sensitivity to distinguish mild from moderate-severe wounds, a low cutoff score was needed. IMPLICATIONS FOR PRACTICE AND RESEARCH: Wound assessment continues to be a subjective exercise, even with the utilization of a tool. Additional research is needed for strategies to support the assessment of surgical wounds in infants. Such tools are needed for future research, particularly when multiple institutions are involved.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".