Optimising fluorescein diacetate sputum smear microscopy for assessing patients with pulmonary tuberculosis
Bibliographic record
Abstract
BACKGROUND: Assessing Mycobacterium tuberculosis (TB) viability by fluorescein diacetate (FDA) microscopy can predict TB culture results, treatment response and infectiousness. However, diverse methods have been published. We aimed to optimise FDA microscopy, minimising sputum processing, biohazard and complexity for use in resource-constrained settings. METHODS AND RESULTS: Optimization: Patients with smear-positive pulmonary TB before treatment and healthy control participants provided sputa. These were divided into equal aliquots that were tested directly or after NaOH centrifuge-decontamination. Each aliquot was cultured and used to prepare slides (n = 80). FDA microscopy used: 1 or 3 drops of sputum; with/out acid-alcohol wash; with/out phenol sterilization; with 0/30/60 seconds KMnO4 quenching. Control samples all had negative culture and microscopy results. FDA microscopy had higher sensitivity when performed directly (without centrifuge-decontamination) on 1 drop of sputum (P<0.001), because 3 drops obscured microscopy. Acid-alcohol wash and KMnO4 quenching made bacilli easier to identity (P = 0.005). Phenol sterilization did not impair microscopy (P>0.1). Validation: The 2 protocols that performed best in the optimization experiments were reassessed operationally by comparing duplicate slides (n = 412) stained with KMnO4 quenching for 30 versus 60 seconds. FDA microscopy results were similar (P = 0.4) and highly reproducible, with 97% of counts agreeing within +/-1 logarithm. Storage: Smear microscopy slides and aliquots of the sputum from which they were made were stored for 4 weeks. Twice-weekly, paired slides (n = 80) were stained with freshly prepared versus stored FDA and read quantitatively. Storing sputum, microscopy slides or FDA solution at 4°C or room temperature had no effect on FDA microscopy results (all P>0.2). Cost: Material costs for each slide tested by FDA microscopy using reagents purchased locally were USD $0.05 and required the same equipment, time and skills as auramine acid-fast microscopy. CONCLUSIONS: We recommend a simple, bio-secure protocol for FDA microscopy that provides sensitive and repeatable results without requiring centrifugation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".