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Record W2513350135 · doi:10.1016/s1473-3099(16)30215-8

Use of standardised patients to assess antibiotic dispensing for tuberculosis by pharmacies in urban India: a cross-sectional study

2016· article· en· W2513350135 on OpenAlex
Srinath Satyanarayana, Ada Kwan, Benjamin Daniels, Ramnath Subbaraman, Andrew McDowell, Sofi Bergkvist, Ranendra Das, Veena Das, Jishnu Das, Madhukar Pai

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Lancet Infectious Diseases · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMcGill University Health Centre
FundersFogarty International CenterCanadian Institutes of Health ResearchGrand Challenges CanadaWorld Bank GroupCanadian Thoracic SocietyNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates Foundation
KeywordsMedicinePharmacyReferralMedical prescriptionCross-sectional studyTuberculosisAntibioticsPediatricsFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: India's total antibiotic use is the highest of any country. Patients often receive prescription-only drugs directly from pharmacies. Here we aimed to assess the medical advice and drug dispensing practices of pharmacies for standardised patients with presumed and confirmed tuberculosis in India. METHODS: In this cross-sectional study in the three Indian cities Delhi, Mumbai, and Patna, we developed two standardised patient cases: first, a patient presenting with 2-3 weeks of pulmonary tuberculosis symptoms (Case 1); and second, a patient with microbiologically confirmed pulmonary tuberculosis (Case 2). Standardised patients were scheduled to present each case once to sampled pharmacies. We defined ideal management for both cases a priori as referral to a health-care provider without dispensing antibiotics or steroids or both. FINDINGS: Between April 1, 2014, and Nov 29, 2015, we sampled 622 pharmacies in Delhi, Mumbai, and Patna. Standardised patients completed 1200 (96%) of 1244 interactions. We recorded ideal management (defined as referrals without the use of antibiotics or steroids) in 80 (13%) of 599 Case 1 interactions (95% CI 11-16) and 372 (62%) of 601 Case 2 interactions (95% CI 58-66). Antibiotic use was significantly lower in Case 2 interactions (98 [16%] of 601, 95% CI 13-19) than in Case 1 (221 [37%] of 599, 95% CI 33-41). First-line anti-tuberculosis drugs were not dispensed in any city. The differences in antibiotic or steroid use and number of medicines dispensed between Case 1 and Case 2 were almost entirely attributable to the difference in referral behaviour. INTERPRETATION: Only some urban Indian pharmacies correctly managed patients with presumed tuberculosis, but most correctly managed a case of confirmed tuberculosis. No pharmacy dispensed anti-tuberculosis drugs for either case. Absence of a confirmed diagnosis is a key driver of antibiotic misuse and could inform antimicrobial stewardship interventions. FUNDING: Grand Challenges Canada, Bill & Melinda Gates Foundation, Knowledge for Change Program, and World Bank Development Research 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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.313
Teacher spread0.276 · 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