Repeatability and clinical utility in stereophotogrammetric measurements of wounds
Bibliographic record
Abstract
Objective: To investigate the hypothesis that stereophotogrammetric wound size monitoring shows suitable inter-observer reliability and user acceptance for clinical practice use. Method: Veterans admitted for conservative management of severe pressure ulcers were eligible for inclusion in the study. Three-dimensional (3D) digital wound images were independently captured by two expert and two non-expert nurse-observers using a commercially available stereophotogrammetry system, weekly for 6 weeks. A double-blinded analyst generated 3D wound reconstructions, using software to determine geometry. Clinical opinion of wound progression was provided by an expert physician. Results: Thirteen wounds were assessed with more than 80% of all images being readable. Interclass correlation of 0.9867 (p < 0.0001) was observed. Compared with clinical opinion, 3D wound measurement was sensitive between improving and static wounds for wound perimeter, volume, depth and length. Conclusion: These preliminary findings suggest that 3D wound measurement minimises differences in wound measurement between expert and non-expert observers, suggesting it could be implemented with high reliability in health-care settings where several observers are involved in wound care management. Declaration of interest: This study was funded in part by a grant from the Department of Veterans Affairs VISN10 Research Initiative Program. All study personnel contributed to the paper. AJD's effort was provided in partial fulfilment of the requirements for the MD degree from Case Western Reserve University School of Medicine. The authors declare they have no conflict of interests with regard to the information presented in this paper.
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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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".