Nucleic Acid Amplification Testing for the Diagnosis of Tuberculosis: Not for All
Bibliographic record
Abstract
To the Editor—There is a problem in extrapolating the results of Laraque et al [1] into the Centers for Disease Control and Prevention (CDC) recommendation regarding nucleic acid amplification (NAA) testing, that “NAA testing should be performed from at least one respiratory specimen from each patient with signs and symptoms of pulmonary TB from whom a diagnosis of TB is being considered but has not yet been established” [2, p. 7]. The area of dispute does not involve those cases in which the acid-fast bacilli (AFB) smear result is positive but those cases in which the result is negative. The value of using NAA testing in these cases will depend on the true incidence of tuberculosis-positive cultures in the population being tested. Using the approximate numbers given in the article by Laraque et al [1], ∼30% of samples submitted for AFB testing had positive culture results. Even in this high-prevalence scenario, 70% of the samples submitted will have negative culture results. Of the culture positive samples, ∼50% (15 of the original 100 samples) had positive smear results. This would result in ∼15 culture-positive samples, of 100 samples received, that would also be tested by NAA. An 80% sensitivity of NAA would lead to 12 culture-positive cases being detected among 85 samples with negative smear results. In most hospital laboratories, the rate of cultures positive for tuberculosis is much lower than this. In my laboratory, only 6 (2%) of 299 specimens submitted for tuberculosis culture had positive results within the past year.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.038 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".