Resistant Plus Susceptible Tuberculosis: The Undiscovered Country
Bibliographic record
Abstract
(See the major article by Zetola et al on pages 1754–63.) Before the emergence of molecular epidemiology techniques, the conceptual approach to tuberculosis treatment seemed straightforward. The clinical laboratory sent the patient's isolate (singular) for testing at a reference laboratory, which would perform a drug-susceptibility test (DST) whose findings were reported as a categorical result (ie, susceptible or resistant). This information then guided selection of appropriate antimicrobial therapy, and in clinical trials, this approach worked in the vast majority of cases. With the advent of molecular typing methods, a core premise of the classical paradigm was challenged. Genetic analyses of cultures, and even patient samples, provided evidence that a minority of patients with tuberculosis harbor >1 strain of bacteria at the same time [1–6] and that some of these mixed infections involve both drug-susceptible and drug-resistant organisms [7, 8]. What then are the consequences for patient management? [9]. In previous reports, mixed infection has been identified as the cause of discrepant DST results in pretreatment isolates [8] and of results that change during the course of therapy [7]. Thus, in patients whose initial phenotypic DST identifies only susceptible isolates, mixed infection can explain the subsequent growth of resistant organisms, offering an alternative explanation to reinfection or acquired resistance. In this situation, a patient would typically receive only first-line medications. Given that these antibiotics have little activity against the subpopulation of resistant organisms, one can readily envision how treatment failure could ensue. In the converse situation, the initial DST identifies drug-resistant organisms, but because of mixed infection there is subsequent growth of susceptible isolates. Is it possible that this situation would also result in an adverse treatment outcome if first-line drugs are withheld from patients with a subpopulation of drug-susceptible organisms? In this issue of the Journal, Zetola et al ask precisely this question.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.033 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".