Evaluation of light emitting diode-based fluorescence microscopy for the detection of mycobacteria in a tuberculosis-endemic region
Bibliographic record
Abstract
OBJECTIVES: To evaluate fluorescence microscopy (FM) using light emitting diode (LED) technology for the detection of acid-fast bacilli at a tertiary referral centre in Mumbai, India, a tuberculosis-endemic country. DESIGN: LED FM was introduced into a laboratory experienced with Ziehl-Neelsen (ZN) microscopy but unfamiliar with FM. It was evaluated in parallel with routine ZN microscopy services and compared with mycobacterial culture as a reference standard. RESULTS: A total of 1357 pulmonary and 917 extra-pulmonary specimens were examined during the study. LED FM had 78.3% sensitivity and 92.0% specificity against mycobacterial culture when using pulmonary specimens, and 34.0% sensitivity and 88.8% specificity for extra-pulmonary specimens. The mean time per smear examination was 2.48 min for ZN vs. 1.41 min for LED FM. Several biases in study design and operation identified during analysis, which are likely to lead to underestimates of LED FM accuracy, are discussed in the context of future LED FM evaluations. CONCLUSIONS: Although LED FM has significant benefits over both ZN microscopy and conventional FM, its implementation and validation may be prone to difficulties which could hamper evaluation of its performance. Adequate training and detailed standard operating procedures are important to maximise accuracy.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".