Diagnosa Penyakit Kulit (Dermatitis) menggunakan Metode Certainty Factor
Bibliographic record
Abstract
Dermatitis is an inflammatory skin disease that is accompanied by itchy skin. It occurs in infants, and gets better in adolescence, but some cases can persist for a long time or even develop the disease in adulthood. In general, if you have this skin disease, you should see a skin specialist for a consultation. However, skin specialists are not always at the hospital, making it difficult for patients to arrange appointments to meet or consult. Therefore, the hospital needs to have an additional system that can help facilitate the medical team to detect the type of dermatitis disease based on symptoms (blistering skin, redness of the skin, scaly and dry skin, dark skin, blistering skin, cracked skin that is present in the disease, atopic dermatitis, contact dermatitis, seberoic dermatitis, nummular dermatitis, and intertriginous dermatitis and get handling and understanding of dermatitis disease using the certainty factor method. From the analysis carried out, the results of the diagnosis of the selected symptoms are obtained, the most accurate diagnosis is Atopic Dermatitis 98% with the treatment given, namely Corticosteroids and Antihistamines to patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".