Comparative cost and performance of light-emitting diode microscopy in HIV–tuberculosis-co-infected patients
Bibliographic record
Abstract
Light-emitting diode (LED) microscopy has recently been endorsed by the World Health Organization (WHO). However, it is unclear whether LED is as accurate and cost-effective as Ziehl-Neelsen (ZN) microscopy or mercury vapour fluorescence microscopy (MVFM) in tuberculosis (TB)-HIV-co-infected subjects. Direct and concentrated sputum smears from TB suspects were evaluated using combinations of LED microscopy, ZN microscopy and MVFM. Median reading time per slide was recorded and a cost analysis performed. Mycobacterial culture served as the reference standard. 647 sputum samples were obtained from 354 patients (88 (29.8%) were HIV-infected and 161 (26%) were culture-positive for Mycobacterium tuberculosis). Although overall sensitivity of LED compared with ZN microscopy or MVFM was similar, sensitivity of all three modalities was lower in HIV-infected patients. In the HIV-infected group, the sensitivity of LED microscopy was higher than ZN microscopy using samples that were not concentrated (46 versus 39%; p = 0.25), and better than MVFM using concentrated samples (56 versus 44; p = 0.5). A similar trend was seen in the CD4 count <200 cells · mL(-1) subgroup. Median (interquartile range) reading time was quicker with LED compared with ZN microscopy (1.8 (1.7-1.9) versus 2.5 (2.2-2.7) min; p ≤ 0.001). Average cost per slide read was less for LED microscopy (US$1.63) compared with ZN microscopy (US$2.10). Among HIV-TB-co-infected patients, LED microscopy was cheaper and performed as well as ZN microscopy or MVFM independent of the staining (ZN or auramine O) or processing methods used.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".