Comparison of sputum collection methods for tuberculosis diagnosis: a systematic review and pairwise and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: The performance of laboratory tests to diagnose pulmonary tuberculosis is dependent on the quality of the sputum sample tested. The relative merits of sputum collection methods to improve tuberculosis diagnosis are poorly characterised. We therefore aimed to investigate the effects of sputum collection methods on tuberculosis diagnosis. METHODS: We did a systematic review and meta-analysis to investigate whether non-invasive sputum collection methods in people aged at least 12 years improve the diagnostic performance of laboratory testing for pulmonary tuberculosis. We searched PubMed, Google Scholar, ProQuest, Web of Science, CINAHL, and Embase up to April 14, 2017, to identify relevant experimental, case-control, or cohort studies. We analysed data by pairwise meta-analyses with a random-effects model and by network meta-analysis. All diagnostic performance data were calculated at the sputum-sample level, except where authors only reported data at the individual patient-level. Heterogeneity was assessed, with potential causes identified by logistic meta-regression. FINDINGS: We identified 23 eligible studies published between 1959 and 2017, involving 8967 participants who provided 19 252 sputum samples. Brief, on-demand spot sputum collection was the main reference standard. Pooled sputum collection increased tuberculosis diagnosis by microscopy (odds ratio [OR] 1·6, 95% CI 1·3-1·9, p<0·0001) or culture (1·7, 1·2-2·4, p=0·01). Providing instructions to the patient before sputum collection, during observed collection, or together with physiotherapy assistance increased diagnostic performance by microscopy (OR 1·6, 95% CI 1·3-2·0, p<0·0001). Collecting early morning sputum did not significantly increase diagnostic performance of microscopy (OR 1·5, 95% CI 0·9-2·6, p=0·2) or culture (1·4, 0·9-2·4, p=0·2). Network meta-analysis confirmed these findings, and revealed that both pooled and instructed spot sputum collections were similarly effective techniques for increasing the diagnostic performance of microscopy. INTERPRETATION: Tuberculosis diagnoses were substantially increased by either pooled collection or by providing instruction on how to produce a sputum sample taken at any time of the day. Both interventions had a similar effect to that reported for the introduction of new, expensive laboratory tests, and therefore warrant further exploration in the drive to end the global tuberculosis epidemic. FUNDING: Wellcome Trust, Joint Global Health Trials consortium, Innovation For Health and Development, and Bill & Melinda Gates Foundation.
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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 | 0.034 | 0.088 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.060 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".