Does Bleach Processing Increase the Accuracy of Sputum Smear Microscopy for Diagnosing Pulmonary Tuberculosis?
Bibliographic record
Abstract
Bleach digestion of sputum prior to smear preparation has been reported to increase the yield of microscopy for diagnosing pulmonary tuberculosis, even in high-HIV-prevalence settings. To determine the diagnostic accuracy of bleach microscopy, we updated a systematic review published in 2006 and applied the Grading of Recommendations Assessment, Development, and Evaluation framework to rate the overall quality of the evidence. We searched multiple databases (as of January 2009) for primary studies in all languages comparing bleach and direct microscopy. We assessed study quality using a validated tool and heterogeneity by standard methods. We used hierarchical summary receiver operating characteristic (HSROC) analysis to calculate summary estimates of diagnostic accuracy and random-effects meta-analysis to pool sensitivity and specificity differences. Of 14 studies (11 papers) included, 9 evaluated bleach centrifugation and 5 evaluated bleach sedimentation. Overall, examination of bleach-processed versus direct smears led to small increases in sensitivity (for bleach centrifugation, 6% [95% confidence interval [CI] = 3 to 10%, P = 0.001]; for bleach sedimentation, 9% [95% CI = 4 to 14%, P = 0.001]) and small decreases in specificity (for bleach centrifugation, -3% [95% CI = -4% to -1%, P = 0.004]; for bleach sedimentation, -2% [95% CI = -5% to 0%, P = 0.05]). Similarly, analysis of HSROC curves suggested little or no improvement in diagnostic accuracy. The quality of evidence was rated very low for both bleach centrifugation and bleach sedimentation. This updated systematic review suggests that the benefits of bleach processing are less than those described previously. Further research should focus on alternative approaches to optimizing smear microscopy, such as light-emitting diode fluorescence microscopy and same-day sputum collection strategies.
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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 | 0.087 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.025 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".