Reliability and Validity of the Revised Photographic Wound Assessment Tool on Digital Images Taken of Various Types of Chronic Wounds
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the validity and reliability of the revised Photographic Wound Assessment Tool (revPWAT) on digital images taken of various types of chronic, healing wounds. SETTING: This multicenter trial was performed in a variety of settings where chronic wounds are assessed. PARTICIPANTS: A total of 206 different photographs taken of 68 individuals with 95 chronic wounds of various etiologies were reviewed in this study. Wound etiologies included people with venous/arterial leg wounds (n = 13), diabetic foot wounds (n = 18), pressure ulcers (n = 32), and wounds of other etiologies (n = 5). MAIN OUTCOME MEASURES: An initial wound assessment using the revPWAT was performed at the bedside, and 3 digital photographs were taken-2 within 72 hours when no change had occurred, and a third was taken 3.5 to 6 weeks later. MAIN RESULTS: The revPWAT scores derived from photographs assessed by the same rater on different occasions and by different raters showed moderate to excellent intrarater intraclass correlation coefficients (ICCs) (ICC = 0.52-0.93), as well as test-retest (ICC = 0.86-0.90) and interrater (ICC = 0.71) reliability. There was excellent agreement between bedside assessments and assessments using photographs (ICC = 0.89). CONCLUSION: The revPWAT is a valid and reliable tool to assess chronic wounds of various etiologies where digital images are viewed.
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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".