Bibliographic record
Abstract
Introduction: The skin of the body is most visible and accessible organ which function function as an important diagnostic aperture to the disease that affects internal organs including the renal system. In CRF some skin changes like hyper pigmentation, xerosis, pruritus and acquired perforating dermatosis may be present regardless of hemodialysis. “Bullous dermatoses of hemodialysis”and “Nephrogenic fibrosing dermopathy may develop resulting in beginning of hemodialysis. For life‑expectancy prolonged treatment with hemodialysis allows newer cutaneous manifestations to appear. Many studies show that about 50-100% of patients with renal disease are associated by at least one dermatological disorder. Therefore early diagnosis of skin disorders and prompt initiation of treatment can be helpful for alter their course and even save a patient’s life. Aim: The main objective of this study is to investigate dermatological Manifestations in patients with Chronic Renal Failure (CRF). Material and method: Total 100 patients with chronic renal failure (CFR) patients with both gender and age between 20 years to 70 years were included in this study during one year of time period. Out of total 100 CRF patients it was divided in to two groups as dialytic group and nondialytic group to compare dermatological manifestations. From all the patients detail history was taken and physical examination was done in each group for the presence of mucocutaenous manifestations which was recorded as data. Different investigation like CBP, RFT, CUE, RBS, serum calcium and phosphorous including chest X‑ray, and ultrasonography were done as well as laboratory investigations like Gram stain, Tzanck smear, KOH mount, and skin biopsies were done. Result: Total patients were divided in to two groups as dialytic group and nondialytic group to compare dermatological manifestations with each group 50 patients. The patients age range from 20 years to 70 years in which 41–50 years in the dialytic group and 31–40 years in the nondialytic group were maximum. Diabetes mellitus was most common underlying etiology causing CRF. In 58% Nonspecific dermatological manifestations xerosis was observed whereas 57% pallor, 38% pruritus , 30% pigmentation and 4% purpura was observed respectively. Conclusion: In CRF patients as total number of dialysis increases the prevalence of xerosis, nail changes and diffuse hyperpigmentation increases. Therefore in CRF patient’s cutaneous marker in the absence of a primary dermatological problem causes so early diagnosis to rule out by kidney disease is necessary by investigations like hematological, urine, and radiological investigations. Keywords: Chronic renal failure (CRF), Dermatology, dialysis, nondialytic
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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 | 0.001 |
| 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) | 0.002 | 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".