ArticleBMC infectious diseases2019
Effect of two alternative methods of pooling sputum prior to testing for tuberculosis with genexpert MTB/RIF.
Article in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Systematic Review of Pooling Sputum as an Efficient Method for Xpert MTB/RIF Tuberculosis Testing during the COVID-19 Pandemic.Emerging infectious diseases · 2021Pooled it
- Population-wide active case finding as a strategy to end TB.The Lancet regional health. Western Pacific · 2024Review
- Pooling Sputum Samples for Efficient Mass Tuberculosis Screening in Prisons.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- The Value of GeneXpert MTB/RIF for Detection in Tuberculosis: A Bibliometrics-Based Analysis and Review.Journal of analytical methods in chemistry · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundPooling sputum specimens is one potential strategy for reducing the cost of using Xpert MTB/RIF, a rapid polymerase chain reaction (PCR)-based test, for the diagnosis of pulmonary tuberculosis. We sought to compare the sensitivity of two alternative method of pooling.
methodsPatients referred for assessment for TB, whose initial sputum was Xpert MTB positive, were recruited and their sputum specimens were pooled for analysis with sputum specimens that were Xpert MTB negative. Two alternative pooling strategies were employed: one in which the concentration of sample reagent (buffer) was maintained at 2:1 (standard), in accordance with the manufacturer's instructions, and another in which the concentration of sample reagent was reduced to 1:1.
resultsWe tested 101 Xpert MTB positive sputum specimens. Among these, 96% of valid test results (95% confidence interval (CI) 89-99%) were positive using the "standard buffer method". Using the "reduced buffer pooling" method 94% of valid test results (95% CI 87-98%) were positive. McNemar's test for the difference in paired proportions did not reach statistical significance (P = 0.56).
conclusionWe have confirmed that pooling of two sputum specimens for testing in a single cartridge is a valid method of reducing the number of cartridges required when using Xpert MTB to detect pulmonary tuberculosis. Two alternative pooling strategies tested here yielded similar results.
trial registrationThe present study was conducted within the Active Casefinding in Tuberculosis (ACT3) Trial. The ACT3 Trial had been registered with Australian and New Zealand Clinical Trials Register on 8th April, 2014. The trial registration number is ACTRN12614000372684 . (Retrospectively registered).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.