Limitations of studying keloid scars using the nude athymic mouse model
Bibliographic record
Abstract
Keloid scars are benign fibroproliferative growths that respond poorly to treatment. This study sought to determine the efficacy of three different glucocorticoids (triamcinolone, methylprednisolone and dexamethasone) in altering human keloid scar tissue implanted in athymic mice. Keloid tissue obtained from three patients (one man and two women) who sought cosmetic removal of their scars was implanted into athymic mice for a duration of 15 or 30 days. The keloid tissue was examined histopathologically and evaluated by a dermatopathologist who was blinded to sample identity and who was using predetermined qualitative scoring criteria. The appearance of central calcification, granulation tissue, foreign body granulomatous reaction and acute inflammatory reaction complicated the comparison of the keloid tissue samples. However, on the basis of observations reported in the present paper, it appears that triamcinolone should remain the treatment of choice for keloid scars. The athymic mouse model that is used for studying keloid scars is the best available approach to in vivo studies; however, limitations identified in this study confound the interpretation of experimental data. Ideally, promising and novel therapies should be investigated clinically.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".