Isoniazid-resistant tuberculosis: A problem we can no longer ignore
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,008 |
| Communication savante | 0,006 | 0,015 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,062 | 0,066 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».