A protocol for a systematic review of the diagnostic accuracy of Loop-mediated-isothermal AMPlification (LAMP) in diagnosis of invasive meningococcal disease in children
Bibliographic record
Abstract
BACKGROUND: Meningococcal disease (MD) is notoriously difficult to diagnose in the early stages of the illness and presents similarly to many self-limiting viral infections. This mandates a cautious approach to diagnosis and initial management of suspected MD with many children receiving precautionary broad-spectrum intravenous antibiotics. Despite this approach, some children are still diagnosed late. In the last 10 years, there have been advances in nucleic acid amplification techniques, and there is now a rapid test that can detect meningococcal DNA in under 30 min. This Loop-mediated-isothermal AMPlification (LAMP) technology may make it possible to diagnose MD at initial presentation thereby greatly improving outcomes and minimising harms through unnecessary treatment. The aim of this systematic review is to determine the diagnostic accuracy of LAMP technology in cases of suspected MD. The review has been registered with PROSPERO [CRD42017078026]. METHODS: To identify relevant studies, we will search MEDLINE, Embase, Web of Science, Scopus and The Cochrane Library. In additional, we will hand-search reference lists and grey literature including contacting the manufacturers of commercially available LAMP tests for MD for any unpublished data. Two reviewers will independently screen study eligibility and extract data. Methodological quality will be assessed, by two authors, according to the revised tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2); any discrepancies will be resolved by a third author. The following test characteristics will be extracted into 2 × 2 tables for all included studies: true positives, false positives, true negatives, and false negatives. Study-specific estimates of sensitivity and specificity with 95% confidence intervals will be displayed in forest plots. To investigate heterogeneity, we will include covariates such as age, sample type, and study type into a bivariate random-effects model. DISCUSSION: This review will help determine the diagnostic accuracy of LAMP technology in diagnosing MD from blood, CSF and throat swabs in children. The data will help to define where in the diagnostic pathway LAMP could be useful including potential as a point-of-care test for children at first presentation.
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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 | 0.075 | 0.136 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.092 | 0.009 |
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".