Comparison of LED and Conventional Fluorescence Microscopy for Detection of Acid Fast Bacilli in a Low-Incidence Setting
Bibliographic record
Abstract
INTRODUCTION: Light emitting diode fluorescence microscopes have many practical advantages over conventional mercury vapour fluorescence microscopes, which would make them the preferred choice for laboratories in both low- and high-resource settings, provided performance is equivalent. METHODS: In a nested case-control study, we compared diagnostic accuracy and time required to read slides with the Zeiss PrimoStar iLED, LW Scientific Lumin, and a conventional fluorescence microscope (Leica DMLS). Mycobacterial culture was used as the reference standard, and subgroup analysis by specimen source and organism isolated were performed. RESULTS: There was no difference in sensitivity or specificity between the three microscopes, and agreement was high for all comparisons and subgroups. The Lumin and the conventional fluorescence microscope were equivalent with respect to time required to read smears, but the Zeiss iLED was significantly time saving compared to both. CONCLUSIONS: Light emitting diode microscopy should be considered by all tuberculosis diagnostic laboratories, including those in high income countries, as a replacement for conventional fluorescence microscopes. Our findings provide support to the recent World Health Organization policy recommending that conventional fluorescence microscopy be replaced by light emitting diode microscopy using auramine staining in all settings where fluorescence microscopy is currently used.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".