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Record W2770756656 · doi:10.6000/1927-5129.2017.13.98

Human Cutaneous Leishmaniasis in Three Districts of Sindh in Pakistan

2017· article· en· W2770756656 on OpenAlexvenueno aff
Farah Sharif, İmtiaz Ahmad, R. R. Ghazi

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCutaneous leishmaniasisLeishmaniasisMedicineLesionVeterinary medicineDermatologySurgeryPathology

Abstract

fetched live from OpenAlex

This study was conducted to record cases of cutaneous leishmaniasis in human beings from three districts i.e., Dadu, Hyderabad and Jamshoro in Sindh, Pakistan. A total number of 102 subjects of different age and sex groups were put under investigation. Ninety four (92.157%) were found positive for cutaneous leishmaniasis. The highest leishmaniasis infection (41%) was recorded in 21-30 years’ age group. The prevalence of cutaneous leishmaniasis was higher in males, (56%) than in females, (44%). The active lesion was more frequent on face (35%), on legs (26%) and on hands (25%) than other exposed parts of the body. Forty five percent of cutaneous leishmaniasis case were recorded in Dadu, 35 percent in Hyderabad, and 20 percent in Jamshoro.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.373
Teacher spread0.317 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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