Evidence map›Paper›PMID 23846422›Full record

SynthesisFrontiers of medicine2013

Treatment outcomes of pulmonary tuberculosis in the past decade in the mainland of China: a meta-analysis.

Xiangwei Li, Yu Yang, Jianmin Liu, Feng Zhou, Wei Cui, Ling Guan, Fei Shen, Cong Gao, Mufei Li, Qi Jin and 1 more

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Frontiers of medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Observational
  6. 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

11 authors.

Xiangwei LiMOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100176, China.
Yu Yang
Jianmin Liu
Feng Zhou
Wei Cui
Ling Guan
Fei Shen
Cong Gao
Mufei Li
Qi Jin
Lei Gao

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the implementation of directly observed treatment strategy (DOTS), China has made a significant achievement in tackling the tuberculosis (TB) epidemic in the 1990s. However, only half of regions in China met or exceeded the 85% rate of treatment success target. The aim of the present study is to summarize the treatment outcomes of smear-positive pulmonary TB in the mainland of China in the past decade using metaanalysis based on systematic review of published observational studies. A total of 50 eligible articles (58 studies) were identified and included in this study. The summarized treatment success rates were 93.9% (95% CI, 92.8%-94.7%) for new cases and 85.4% (95% CI, 83.0%-87.6%) for previously treated cases, and the summarized cured rate were 92.2% (95% CI, 90.9%-93.3%) and 81.2% (95% CI, 79.1%-83.1%), respectively. A remarkable increase of rates for treatment success and cure was observed in the 1990s. After 2000, the summarized treatment outcomes were tending towards stability. In addition, geographic areas, type of the data and administrative level of the hospital were also found to influence the estimates of the treatment outcomes. Results of the present study clearly show, in general, that the pulmonary TB treatment achieved significant success in the past decade in the mainland of China. However, it needs to be further strengthened in the central and west areas.

Indexed as

Directly Observed TherapyAntitubercular AgentsChinaEvidence-Based MedicineHumansPrevalenceTreatment OutcomeTuberculosis, Multidrug-ResistantTuberculosis, PulmonaryAntitubercular Agents

Identifiers

What OpenQuestion holds

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Registered trials

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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.