A Pilot Study of Short-Duration Sputum Pretreatment Procedures for Optimizing Smear Microscopy for Tuberculosis
Bibliographic record
Abstract
BACKGROUND: Direct sputum smear microscopy for tuberculosis (TB) lacks sensitivity for the detection of acid fast bacilli. Sputum pretreatment procedures may enhance sensitivity. We did a pilot study to compare the diagnostic accuracy and incremental yield of two short-duration (<1 hour) sputum pretreatment procedures to optimize direct smears among patients with suspected TB at a referral hospital in India. METHODOLOGY/FINDINGS: Blinded laboratory comparison of bleach and universal sediment processing (USP) pretreated centrifuged auramine smears to direct Ziehl-Neelsen (ZN) and direct auramine smears and to solid (Loweinstein-Jensen (LJ)) and liquid (BACTEC 460) culture. 178 pulmonary and extrapulmonary TB suspects were prospectively recruited during a one year period. Thirty six (20.2%) were positive by either solid or liquid culture. Direct ZN smear detected 22 of 36 cases and direct auramine smears detected 26 of 36 cases. Bleach and USP centrifugation detected 24 cases each, providing no incremental yield beyond direct smears. When compared to combined culture, pretreated smears were not more sensitive than direct smears (66.6% vs 61.1 (ZN) or 72.2 (auramine)), and were not more specific (92.3% vs 93.0 (ZN) or 97.2 (auramine). CONCLUSIONS/SIGNIFICANCE: Short duration sputum pretreatment with bleach and USP centrifugation did not increase yield as compared to direct sputum smears. Further work is needed to confirm this in a larger study and also determine if longer duration pre-treatment might be effective in optimizing smear microscopy for TB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".