Does solid culture for tuberculosis influence clinical decision making in India?
Bibliographic record
Abstract
SETTING: Medical units at an academic tertiary referral hospital in Southern India. OBJECTIVE: To investigate the impact of solid culture on Löwenstein-Jensen medium on clinical decision making. DESIGN: In a retrospective review of 150 culture-positive and 150 culture-negative consecutively sampled tuberculosis (TB) suspects, treatment decisions were analysed at presentation, after the availability of culture detection results and after the availability of drug susceptibility testing (DST) culture results. RESULTS: A total of 124 (82.7%) culture-positive patients and 35 (23.3%) culture-negative patients started anti-tuberculosis treatment prior to receiving their culture results; 101 patients (33.7%) returned for their results; two (1.3%) initiated treatment based on positive culture and no culture-negative patients discontinued treatment. DST was performed on 119 (79.3%) positive cultures: 30 (25.2%) showed any resistance, eight (6.7%) showed multidrug resistance and one (0.84%) showed extensively drug-resistant TB. Twenty-eight patients (23.5%) returned for their DST results. Based on DST, treatment was modified in four patients (3.4%). CONCLUSION: Using solid culture, 150 cultures need to be tested for one treatment modification and 30 for DST. The cost of the widespread application of culture will need to be balanced against its impact on treatment decisions in India.
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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 | 0.004 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".