International validation of a venous leg ulcer risk assessment tool
Bibliographic record
Abstract
OBJECTIVE: To internationally validate a tool for predicting the risk of delayed healing of venous leg ulcers (VLUs). METHOD: A 10-item tool including sociodemographic factors, venous history, ulcer and lower limb characteristics, compression and mobility items to determine the risk of delayed healing of VLUs has previously been developed and validated in Australia. This study prospectively validated this tool using receiver operating characteristic (ROC) methods; using the area under the curve (AUC) to quantify the discriminatory capability of the tool to analyse the international populations of the UK, Austria and New Zealand. RESULTS: The validation of the tool in the UK, Austria and New Zealand has indicated that the model has moderate discrimination and goodness-of-fit with an AUC of 0.74 (95% CI: 0.66-0.82) for the total risk assessment score. CONCLUSION: The international validation of a risk assessment tool for delayed healing of VLUs will allow clinicians globally to be able to determine realistic outcomes from an early assessment and to be able to guide early tailored interventions to address the specific modifiable risk factors and thus promote timely healing.
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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 | 0.037 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".