Re: Estimating the Early Transmission Inhibition of New Treatment Regimens for Drug-Resistant Tuberculosis
Bibliographic record
Abstract
TO THE EDITOR—We read with interest this article by the late Stoltz et al [1] and think the method described could generate useful information for TB programmes. However, we think the precision of these estimates of infectiousness has been overstated; that, given the limits of the current evidence around duration of infectiousness after initiation of effective treatment, we favor an approach to deisolation that prioritizes an overall risk assessment; and that, before such experiments are repeated, we need better methods for ensuring the safety of research participants. The purpose of the study [1] was to estimate differences in infectiousness between people with tuberculosis, not differences in susceptibility to Mycobacterium tuberculosis between guinea pigs. As it was not possible to ascertain which person infected which guinea pig, this was a comparison between 1 versus 1 cohort rather than a comparison between 5 versus 9 people. Either way, P < .0001 is implausible.
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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 | 0.021 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".