Evaluation of the performance and feasibility of RLDT in detecting <i>Shigella</i> in a primary healthcare facility of rural Bangladesh
Bibliographic record
Abstract
Abstract Shigellosis remains underdiagnosed due to the lack of rapid, reliable diagnostics. Although Shigella spp. is known for causing dysentery, over 50% of Shigella -associated cases are watery diarrhea. Identifying and treating these cases of watery diarrhea caused by Shigella could be lifesaving, which would require a point-of-care (POC) Shigella test. Evidence-based treatment could also reduce the overuse of antibiotics. We evaluated the feasibility and applicability of the Rapid LAMP-based Diagnostic Test (RLDT) assay for detecting shigellosis in a healthcare facility. Stool samples (n=228) were collected from children seeking facility care during diarrhea and on their follow-up visits from an ongoing case-control study, INSIGHT. The stool samples were tested by the INSIGHT lab personnel using the RLDT for Shigella spp. The lab personnel at a rural primary healthcare facility, Mirzapur Upazila Health Complex (MUHC) in Bangladesh, received training in RLDT and retested the stool samples with RLDT at the MUHC; the results were compared. The acceptance of RLDT at MUHC was also evaluated through questionnaires. After training, the MUHC lab personnel independently performed RLDT. The RLDT tests performed by the MUHC showed sensitivity of 98% and specificity of 99%, with an almost perfect agreement (Kappa = 0.96) compared with the pre-tested RLDT results. The RLDT assay was well received by the MUHC, despite the general challenges of limited manpower and resources at rural health care facilities. This study demonstrates the potential of using RLDT as a POC test in Shigella -endemic countries to support evidence-based treatment, saving lives and reducing inappropriate antibiotic use. Keypoints RLDT, as a simple and accurate diagnostic tool, offers a practical solution for Shigella detection in low-resource settings. Scaling up RLDT will empower health systems by enabling timely diagnoses to guide treatment and reduce antimicrobial resistance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".