Bates-Jensen Wound Assessment Tool
Bibliographic record
Abstract
PURPOSE: A group of 3 WOC nurses and a nurse researcher, in partnership with the author of the Bates Wound Assessment Tool (BWAT), sought to validate wound photographs depicting each characteristic of the instrument. INSTRUMENT: The BWAT contains 13 items that assess wound size, depth, edges, undermining, necrotic tissue type, amount of necrotic, granulation and epithelialization tissue, exudate type and amount, surrounding skin color, edema, and induration. These are rated using a modified Likert scale; a score of 1 indicates the healthiest and 5 indicates the most unhealthy attribute for each characteristic. In 2001, the PSST was revised and renamed the Bates-Jensen Wound Assessment Tool to reflect the global use of the tool with wound types beyond pressure ulcers. METHODS: Phase 1 involved the selection of digitalized wound photographs for 11 of the BWAT wound characteristics by the researchers. The photographs needed to be of high resolution and good quality for eventual publication and validated by the original BWAT author as being representative of the intended characteristic. In phase 2, a face-to-face validation exercise was completed to include, edit, or exclude these photographs. Corrections were made; additional photographs were obtained for the remaining characteristics and to replace those not validated. Phase 3 involved an electronic survey that achieved validation online. PARTICIPANTS: Phase 2 participants consisted of 15 WOC nurses with a mean of 11.5 years of experience with wounds. Phase 3 had 8 WOC nurses and 1 master's prepared wound care specialist, with approximately 10 years of experience. One third of participants in each phase were familiar with the BWAT. In a separate exercise to rate photographs that would be used for testing the implementation of the pictorial guide, 7 WOC nurses and 2 RNs who used the BWAT regularly and 2 researchers participated in a face-to-face discussion. RESULTS: A total of 214 photographs were reviewed in this study. Seventy-three percent (n = 55) of the photographs for the pictorial guide were endorsed in phase 2, and 100% (n = 53) in phase 3 to demonstrate the 65 BWAT characteristics. In addition, photographs that could be used for a competency exercise and for pre- and posttests were also rated by the panels. CONCLUSIONS: The photographic content of the BWAT pictorial guide has been validated by a small group of wound care experts. The purpose of the exercise was to create a visual learning aid to enhance the education around wound assessment and as a resource for nurses in practice. Now published in a pocket guide format, it is a standardized way to teach BWAT wound assessment skills in a consistent format.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".