Use of simulated sputum specimens to estimate the specificity of laboratory-diagnosed tuberculosis.
Bibliographic record
Abstract
SETTING: Cross-contamination is not uncommon in mycobacteriology laboratories of high-income countries, as documented by bacterial genotyping. The extent of this problem in low-income countries is largely unknown, where this method is impractical. OBJECTIVE: To estimate the rate of cross-contamination in a high-volume tuberculosis (TB) laboratory in South Africa. DESIGN: Simulated sputum specimens labelled with false names were sent from a TB clinic, interspersed with patient samples, and processed for culture and microscopy. Results were interpreted in the context of the observed proportion of samples with positive microscopy and culture results. RESULTS: With microscopy, 6/190 (3.2%) simulated specimens were positive (estimated specificity = 96.8%). Considering the 881 positive microscopy results in 6093 clinical samples, we extrapolate that 19.3% (95%CI 7.0-42.8) of positive smears were false-positives. On culture, 2/190 (1.1%) of the simulated specimens were positive for Mycobacterium tuberculosis (estimated specificity = 98.9%). Considering the 1862 positive cultures from 6093 clinical samples, we estimate that 2.4% (95%CI 0.3-8.8) of positive cultures were false-positives. CONCLUSION: Simulated specimens offer a simple means of estimating the proportion of false-positive results, providing information on all sources of potential error from the clinic, through the laboratory and to reporting of results.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".