Evidence map›Paper›PMID 21996693›Full record

ReviewThe Journal of infectious diseases2011

Alternative methods of diagnosing drug resistance--what can they do for me?

David A J Moore, N Sarita Shah

Abstract readReview
In one paragraph

Review in The Journal of infectious diseases, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Observational
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
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

2 authors.

David A J MooreDepartment of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom. david.moore@lshtm.ac.uk
N Sarita Shah

Funding

Wellcome Trust
6 · The paper itself

Abstract

During the last decade considerable attention has been focussed upon the development of new technologies and methodologies for detection of drug resistance in Mycobacterium tuberculosis. There is a growing acknowledgement that the redundancy in testing a full panel of first-line drugs is an unaffordable indulgence; since only resistance at baseline to either (or both) of the two most potent agents, isoniazid (H) and rifampicin (R), would usually prompt therapeutic modification there is a shift towards initial RH (or R alone for selected genotypic technologies) drug susceptibility testing (DST) followed, if necessary by further extended first and second line agent (currently phenotypic) DST. Most of the new drug susceptibility tests endorsed by the World Health Organization since 2007 deliver rapid RH (or R alone for selected genotypic technologies) DST. Targeting of patient groups with risk factors for drug resistance increases the proportion of tests that identify drug resistance, but in many settings at least as many patients with drug resistant disease will have no identifiable risk factors--equity of care demands that universal RH DST at baseline should be the goal. We review the bewildering array of choices facing TB program directors and attempt to provide objective information to help in deciding what tools may be best suited to different environments.

Indexed as

Antitubercular AgentsHumansMicrobial Sensitivity TestsMycobacterium tuberculosisTuberculosis, Multidrug-ResistantAntitubercular Agents

Identifiers

PMID21996693
PMCPMC3192546

What OpenQuestion holds

Textmetadata
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.