Isoniazid-resistant tuberculosis: A problem we can no longer ignore
Bibliographic record
Abstract
For decades, people working in tuberculosis (TB) knew that monoresistance to isoniazid (INH) was common.INH has been in clinical use since the 1950s, and drug resistance was expected because its use became widespread.But this knowledge did not necessarily lead to testing for INH-resistant, rifampicin-susceptible TB (Hr-TB) or to the use of special drug regimens for this form of TB.Indeed, for decades, no drug-susceptibility testing (DST) for any drug was done unless patients failed first-line therapy or had risk factors for drug-resistant TB (DR-TB).Simply put, we chose to ignore the problem.When the TB world woke up to the need for universal DST and included it as a key goal in the End TB Strategy released in 2015, the focus became rapid testing for rifampicin resistance (RR) as a means of achieving universal DST.Novel technologies such as Xpert MTB/RIF (Cepheid, Sunnyvale, CA, USA) were rolled out in 2010, but the technology did not include INH-resistance testing [1].Even today, access to any DST remains low, and when performed, DST is often limited to RR [2].In 2020, we can no longer hide from this worrisome problem because Hr-TB is much more common than RR and could seriously jeopardize progress in the fight against TB.This is confirmed by an analysis of aggregated drug resistance data from 2003 to 2017 across 156 countries presented in the accompanying research study by Anna Dean and colleagues in PLOS Medicine, showing that-on average-7.4%(95% CI 6.5-8.4) of new cases and 11.4% (9.4-13.4) of previously treated patients have Hr-TB [3].The overall prevalence of INH resistance (with or without concomitant RR) ranged between 10.7% (9.6-11.9)and 27.2% (24.6-29.9)depending on the treatment history and reached even more alarming levels in certain countries, particularly in the European and Western Pacific regions.The analysis by Dean and colleagues highlights major flaws in national surveillance systems, which go hand in hand with limited laboratory capacity.The small sample sizes available from some countries make national prevalence estimates imprecise.Furthermore, the diversity of detection methods employed across settings along with the widespread lack of quality control underscores the need for improved surveillance by countries.From a clinical standpoint, if INH resistance is not detected, new patients are managed as if they had pansusceptible TB, with a substantially increased risk of treatment failure or relapse and a greater propensity to acquire further resistance [4].Yet, most research and policy efforts so far have been focused solely on RR as a proxy for multidrug-resistant (MDR)-TB.This means that hundreds of thousands of patients with Hr-TB are staying in the shadows, not receiving appropriate care, and all too often ending up developing MDR-TB.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.062 | 0.066 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".