Evaluation of LAMP for detection of Shigella from stool samples in children
Bibliographic record
Abstract
Background. To assess the diagnostic accuracy of loop-mediated isothermal amplification (LAMP) for the detection of Shigella from stool samples from children. Methods. Consecutive stool samples from children aged <13 years old who presented with acute watery diarrhoea or dysentery to the Department of Paediatrics were collected and processed in the Department of Microbiology. All the stool samples were subjected to culture, conventional PCR and LAMP. Genomic sequencing was performed for samples that were positive by LAMP but negative by both culture and conventional PCR. The LAMP results were compared to those from culture and to a composite reference standard based on culture and conventional PCR. Results. Amongst the 374 stool samples tested, 291 samples were positive by LAMP and 213 were positive by the composite reference standard. The sensitivity of LAMP was 100 % (98.3–100 %) and its specificity was 51.6 % (43.6–59.5 %) with a disease prevalence of 57 %. The sensitivity and specificity of LAMP improved to 99.3 % (94.2–100) and 98.2 % (94.5–99.9), respectively, using latent class analysis, while assuming that genomic sequencing has perfect specificity. Discussion. The authors have standardized the LAMP procedure for direct application to clinical stool samples. LAMP is a sensitive and specific method for the diagnosis of Shigella from stool samples of children as compared to both culture and conventional PCR.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".