Evidence map›Paper›PMID 38384625›Full record

ReviewCureus2024

A Comprehensive Review on Long vs. Short Regimens in Multidrug-Resistant Tuberculosis (MDR-TB) Under Programmatic Management of Drug-Resistant Tuberculosis (PMDT).

Ashwin Karnan, Ulhas Jadhav, Babaji Ghewade, Anjana Ledwani, Poorna Shivashankar

Abstract readReview
In one paragraph

Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Observational
  10. Article
  11. Article
  12. Review
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ashwin KarnanRespiratory Medicine, Jawaharlal Nehru Medical College, Wardha, IND.
Ulhas JadhavRespiratory Medicine, Jawaharlal Nehru Medical College, Wardha, IND.
Babaji GhewadeRespiratory Medicine, Jawaharlal Nehru Medical College, Wardha, IND.
Anjana LedwaniRespiratory Medicine, Jawaharlal Nehru Medical College, Wardha, IND.
Poorna ShivashankarRespiratory Medicine, Jawaharlal Nehru Medical College, Wardha, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This comprehensive review delves into the intricate landscape of multidrug-resistant tuberculosis (MDR-TB) treatment within the programmatic management of drug-resistant tuberculosis (PMDT) framework. MDR-TB poses a substantial global health threat, necessitating targeted approaches for effective management. The analysis explores the historical evolution, efficacy, safety profiles, and implementation challenges associated with long and short regimens. The findings underscore the importance of individualized clinical practices, considering patient-specific factors, and the need for ongoing monitoring within PMDT programs. Recommendations advocate for integrating advanced diagnostics, continuous surveillance, and training for healthcare professionals. The review concludes with a nuanced outlook on long versus short regimens, emphasizing a balanced approach and the imperative role of collaborative efforts in shaping the future of MDR-TB treatment. This synthesis contributes to the ongoing discourse, providing valuable insights for healthcare practitioners, policymakers, and researchers working toward optimizing outcomes for individuals afflicted with MDR-TB.

Indexed as

healthcare program optimizationlong regimenmultidrug-resistant tuberculosis (mdr-tb)programmatic management of drug-resistant tuberculosis (pmdt)short regimentreatment efficacy

Identifiers

PMID38384625
PMCPMC10879947

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.