LEPROSY A CHANGE IN PROFILE?
Bibliographic record
Abstract
Leprosy has been ofcially eliminated from India since December, 2005; still there are districts and blocks reporting high prevalence indicating ongoing transmission. Thepresent study aimed at determining the current situation/scenario of leprosy in a tertiary level hospital in Kolkata.It is a cross-sectional observational study carried out on patients diagnosed and registered in the leprosy clinic (May 2018-May 2020).Data regarding clinical features, histopathological diagnosis and treatment, reactions was analyzed. Skin biopsies were taken in all cases and slit skin smear was done. The biopsies and slit skin smear were evaluated for the type of pathology and acid fast bacilli (AFB) status. A total of 58 patients were registered over 8months period, with M: F (4.8:1). 5child cases were reported, 44 (75.86%) were new cases, 14 (24.14%) were defaulter. Slit skin smear showed 37 (63.79%) cases were multibacillary (MB). Lepromatous leprosy (LL) 28 (48.28%) was the most frequent morphologic type followed by borderline tuberculoid (BT) 12 (20.69%) andtuberculoid leprosy 9 (15.52%) borderline lepromatous(BL) 4 (6.89%) cases, 5 (8.62%) case of histoid leprosy. 9 (15.51%) presented with each type 1 and 5 (8.62%) cases with type 2Erythema NodosumLeprosum (ENL) reaction.Our studyoffers insight into the current status of the disease in the area of otherwise low prevalence. It is seen that despite statistical elimination, lepromatus leprosy, leprosy reactions are commonly seen as presenting features. It highlights the need for continuation of targeted leprosy control activities and active case detection.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".