Further Evidence of Inadequate Quality in Lateral Flow Devices Commercially Offered for the Diagnosis of Rabies
Bibliographic record
Abstract
human deaths annually, primarily affecting low- and middle-income countries in Asia and Africa. In those regions, insufficient surveillance is hampering adequate medical intervention and is driving the vicious cycle of neglect. Where resources to provide laboratory disease confirmation are limited, there is a need for user-friendly and low-cost reliable diagnostic tools that do not rely on specialized laboratory facilities. Lateral flow devices (LFD) offer an alternative to conventional diagnostic methods and may strengthen control efforts in low-resource settings. Five different commercially available LFDs were compared in a multi-centered study with respect to their diagnostic sensitivity and their agreement with standard rabies diagnostic techniques. Our evaluation was conducted by several international reference laboratories using a broad panel of samples. The overall sensitivities ranged from 0% up to 62%, depending on the LFD manufacturer, with substantial variation between the different laboratories. Samples with high antigen content and high relative viral load tended to test positive more often in the Anigen/Bionote test, the latter being the one with the best performance. Still, the overall unsatisfactory findings corroborate a previous study and indicate a persistent lack of appropriate test validation and quality control. At present, the tested kits are not suitable for in-field use for rabies diagnosis, especially not for suspect animals where human contact has been identified, as an incorrect negative diagnosis may result in human casualties. This study points out the discrepancy between the enormous need for such a diagnostic tool on the one hand, and on the other hand, a number of already existing tests that are not yet ready for use.
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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 | 0.034 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".