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Record W4404336929 · doi:10.61132/saturnus.v2i4.331

Diagnosa Penyakit Kulit (Dermatitis) menggunakan Metode Certainty Factor

2024· article· en· W4404336929 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSaturnus · 2024
Typearticle
Languageen
FieldComputer Science
TopicComputer Science and Engineering
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMedicineTraditional medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.230
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it