A comparison of computer-assisted and manual wound size measurement.
Bibliographic record
Abstract
Accurate and precise wound measurements are a critical component of every wound assessment. To examine the reliability and validity of a new computerized technique for measuring human and animal wounds, chronic human wounds (N = 45) and surgical animal wounds (N = 38) were assessed using manual and computerized techniques. Using intraclass correlation coefficients, intrarater and interrater reliability of surface area measurements obtained using the computerized technique were compared to those obtained using acetate tracings and planimetry. A single measurement of surface area using either technique produced excellent intrarater and interrater reliability for both human and animal wounds, but the computerized technique was more precise than the manual technique for measuring the surface area of animal wounds. For both types of wounds and measurement techniques, intrarater and interrater reliability improved when the average of three repeated measurements was obtained. The precision of each technique with human wounds and the precision of the manual technique with animal wounds also improved when three repeated measurement results were averaged. Concurrent validity between the two techniques was excellent for human wounds but poor for the smaller animal wounds, regardless of whether single or the average of three repeated surface area measurements was used. The computerized technique permits reliable and valid assessment of the surface area of both human and animal wounds.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".