Controlled Study of Mycetoma: Development of Diagnosis, Treatment And Controlled Programme Northern India
Bibliographic record
Abstract
INTRODUCTION: Mycetoma is also known as Madura foot, is a patient fungal infection of the skin and the tissues just under the skin. Mycetoma is also known as Madura foot, is a patient fungal infection of the skin and the tissues just under the skin. ETIOLOGY: The most prevailing causative agent of mycetoma worldwide is Mycetoma Mycetomatis. Etiologic agents of eumycetoma can be classified based on the type of grain they produce, generally classified as black, white or pale unstained or yellow- to -yellow- brown grains. PATHOGENESIS: The pathogens including both bacteria and fungi, infection leads to the causative microorganisms spreading through the fascial planes to the underlying muscles and bones and accordingly to the destruction and deformity of the osseous and muscular tissues and disfiguration. DIAGNOSIS: Diagnosed based on clinical aspects and grain colour. Culture and histopathology are the gold standard methods. Identification of the causative agent can be made by bacteriological culture, biochemical characteristics and a series of technical phenotypic tests. TREATMENT: Eumycetoma is treated with antifungal agents in combination with surgical excision whereas actinomycetoma is treated with antibacterial agents. CONTROL PROGRAMMES OF MYCETOMA: Therapies that work against mycetoma are limited. Actinomycetoma is generally treatable with antibiotics and Eumycetoma is generally treated with long- term antifungal medicine. CONCLUSION: The clinical overview of mycetoma is necessary to raise awareness about its presence due to the large population of immigrants from endemic regions. Since no standard outlined treatment measure has been given by World Health Organization (WHO).
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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 | 0.008 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".