Use of Isoniazid Monotherapy in Comparison to Rifamycin-Based Regimen for the Treatment of Patients With Latent Tuberculosis: A Systematic Review
Bibliographic record
Abstract
Tuberculosis (TB) is a common infectious disease that is present all around the world. This insidious disease needs drastic measures for its eradication. One of the actions contributing to it is the timely diagnosis and offering suitable treatment options for latent tuberculosis patients. In this review, we will discuss and compare the variety of options available for this purpose. We searched PubMed/Medline, Cochrane library, Google Scholar, and Science Direct to find articles regarding the effectiveness, safety, and completion of any of the five regimens available for latent tuberculosis infection. These options are the most classic and standard nine months of isoniazid given daily, which is now more commonly given as six months course, three months of daily isoniazid and rifampin, three months of weekly isoniazid and rifapentine, and four months of daily rifampin. We looked into free full-text studies published from 2011 to 2021 available in English language and human studies. After applying inclusion/exclusion criteria and removing duplicates and screening, 34 articles were shortlisted for quality assessment check, after which we finalized nine studies. Cochrane risk-of-bias assessment tool was used for quality check of randomized control trials, New-Castle Ottawa tool for observational studies, and assessment of multiple systematic reviews (AMSTAR) tool for systematic reviews. Efficacy was checked by tracking down the new cases of TB in the sample population that took the treatment for latent tuberculosis infection. New rifamycin-based regimens were almost equal in effectiveness to isoniazid regimens. The side effect profile is different for both regimens, but short-duration courses tend to have a higher chance of completion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".