MétaCan
Menu
Back to cohort
Record W2797768836 · doi:10.6000/1927-5129.2018.14.15

Study of Multi-Drug Resistance Associated with Anti-Tuberculosis Treatment by DOT Implementation Strategy in Pakistan

2018· article· en· W2797768836 on OpenAlexvenueno aff
Sana Saeed, Moosa Raza, Maryam Shabbir, Muhammad Furqan Akhtar, Ali Sharif, Muhammad Zaman, Sajid Ali, Sajid Nawaz, Ayesha Saeed

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsTuberculosisMedicineRifampicinDrug resistanceIsoniazidDiseaseInternal medicinePopulationDrugMulti-drug-resistant tuberculosisMycobacterium tuberculosisPediatricsEnvironmental healthPharmacologyPathologyBiology

Abstract

fetched live from OpenAlex

Purpose: The present prospective cross sectional study was aimed to access the prevalence and trend of Multi-Drug Resistant Tuberculosis (MDR-TB) in different age groups and gender, in the city of Lahore, Pakistan. Tuberculosis is a disease of poverty affecting mostly young adults in their most productive years; however, all age groups are at risk.Method: The study population consisted of patients under DOT program with MDR-TB among males and females and in different categories of age groups. The data was collected from 100 MDR-TB patients from 7800 TB patients that were admitted in duration of 6 months and analyzed to evaluate the drug resistance associated with patient’s noncompliance. Moreover, drugs resistance ratio was also calculated from the data.Results: TB is a specific infectious disease, caused by M. tuberculosis strains, which is becoming resistant to anti-tuberculosis agents especially to Isoniazid and Rifampicin that are two key drugs of TB treatment and are termed as MDR-TB. The disease was seen in 66% males and 34% in female. The highest drug resistance ratio was in found in adults (age group).

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 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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.409
Teacher spread0.355 · 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 teacher head, 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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Basic & Applied SciencesSame topicTuberculosis Research and EpidemiologyFrench-language works237,207