Use of Rapid, Point-of-Care Assays by Private Practitioners in Chennai, India: Priorities for Tuberculosis Diagnostic Testing
Bibliographic record
Abstract
SETTING: Private practitioners are frequently the first point of healthcare contact for patients with tuberculosis (TB) in India. As new molecular tests are developed for point-of-care (POC) diagnosis of TB, it is imperative to understand these individuals' practices and preferences for POC testing. OBJECTIVE: To evaluate rapid testing practices and identify priorities for novel POC TB tests among private practitioners in Chennai. DESIGN: We conducted a cross-sectional survey of 228 practitioners practicing in the private sector from January 2014 to February 2015 who saw at least one TB patient in the previous year. Practitioners were randomly selected from both the general community and a list of practitioners who referred patients to a public-private mix program for TB treatment. We used standardized questionnaires to collect data on current practices related to point-of-care diagnosis and interest in hypothetical POC tests. We used multivariable Poisson regression with robust estimates of standard error to calculate measures of association. RESULTS: Among 228 private practitioners, about half (48%) utilized any rapid testing in their current practice, most commonly for glucose (43%), pregnancy (21%), and malaria (5%). Providers using POC tests were more likely to work in hospitals (56% vs. 43%, P = 0.05) and less likely to be chest specialists (21% vs. 54%, P<0.001). Only half (51%) of providers would use a hypothetical POC test for TB that was accurate, equipment-free, and took 20 minutes to complete. Chest specialists were half as likely to express interest in performing the hypothetical POC TB test in-house as other practitioners (aPR 0.5, 95%CI: 0.2-0.9). Key challenges to performing POC testing for TB in this study included time constraints, easy access to local private labs and lack of an attached lab facility. CONCLUSION: As novel POC tests for TB are developed and scaled up, attention must be paid to integrating these diagnostics into healthcare providers' routine practice and addressing barriers for POC testing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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.
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".