Evaluation of skin erythema by use of chromametry and image analysis of digital photographs after intradermal administration of histamine in dogs
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the degree of erythema during an induced erythematous reaction, the histamine skin test reaction, can be assessed objectively by use of chromametry and image analysis of digital photographs. ANIMALS: 9 pet dogs (6 Golden Retrievers and 3 yellow Labrador Retrievers). PROCEDURE: Histamine phosphate was injected intradermally, and erythema of the wheal reaction was evaluated during the hour that followed. This was done by use of clinical scores, chromametry, and image analysis of digital photographs. Method reproducibility was tested for visual evaluation of printouts of digital photographs and for image analysis of the same photographs. RESULTS: The coefficient of variation of the technically derived erythema values was < 10%. The reproducibility of image analysis was high and the range of agreement between observers narrow. Using chromametry, it was not possible to differentiate between various degrees of erythema intensity as visually perceived. In contrast, use of image analysis of digital photographs enabled discrimination of slight erythema from moderate and marked erythema. The dynamics of reaction measured by chromametry followed the clinical observation. CONCLUSIONS AND CLINICAL RELEVANCE: Chromametric values are comparable to those obtained by visual inspection. As the result of standardized conditions, chromametry is preferred over digital photography.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".