Acquired perforating dermatoses in patients with diabetic kidney disease on hemodialysis
Bibliographic record
Abstract
Acquired perforating dermatoses (APD) is an uncommon skin disorder seen in patients with diabetes mellitus, chronic kidney disease, or both together. We present the clinicopathological features of APD in patients with diabetic kidney disease and discuss the recent advances in management. We retrospectively analyzed the data of 8 patients with APD presenting to our center. All patients were known cases of Type 2 diabetes and chronic kidney disease requiring maintenance dialysis. Acquired perforating dermatoses was diagnosed based on clinical presentation of itchy, keratotic papulonodular lesions, and characteristic histopathological features of transepithelial elimination on skin biopsy. The patients were subdivided into 4 types of APD based on the biopsy features. All our patients had Type 2 diabetes over 5 years duration and were on maintenance dialysis for more than 6 months before presentation. Acquired perforating dermatoses symptoms appeared 2 to 6 months before presentation. The majority of patients (6/8) had a subtype of reactive perforating collagenosis. All the patients showed significant resolution with topical glucocorticoid therapy. Acquired perforating dermatoses is a skin complication seen in Type 2 diabetes, chronic kidney disease, or when both are present together. Early identification and therapy prevents the associated morbidity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".