Multicentre evaluation of Ziehl-Neelsen and light-emitting diode fluorescence microscopy in China
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility of using light-emitting diode fluorescence microscopy (LED-FM) in peripheral laboratories in China. DESIGN: The performance of LED-FM and Ziehl-Neelsen (ZN) microscopy was compared on slides directly prepared from the sputum of tuberculosis (TB) suspects and follow-up patients on treatment. The examination time, fading of fluorescence-stained slides, average unit cost and qualitative user appraisal of LED-FM were also analysed. RESULTS: Among 11 276 slides, the smear-positive rate for LED-FM was 11.2% (1263/11 276), 2.6% (294/11 276) higher than that of ZN (8.6%, 969/11 276; χ(2) 263.5, P < 0.05). The examination time for LED-FM (120.0 ± 38.9 seconds) was shorter than that for ZN (206.3 ± 75.9 s; t = 28.12, P < 0.05). For smear fading, quantitative and qualitative errors occurred within respectively 7.8 and 7.7 weeks. The average unit costs for ZN and LED-FM were respectively US$2.20 ± 0.58 and US$1.97 ± 0.71 (t = 5.08, P < 0.05). LED-FM was accepted by most laboratory technicians. CONCLUSION: LED-FM compared favourably with ZN, with a higher smear-positive detection rate, a shorter examination time and lower unit examination cost. LED-FM may be an alternative to ZN as a cost-effective method for detecting bacilli in peripheral laboratories in China.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".