Proof-of-concept study for successful inter-laboratory comparison of MLVA results
Why this work is in the frame
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Bibliographic record
Abstract
Multiple-locus variable-number of tandem repeats analysis (MLVA) is widely used for typing of pathogens. Methods such as MLVA based on determining DNA fragment size by the use of capillary electrophoresis have an inherent problem as a considerable offset between measured and real (sequenced) lengths is commonly observed. This discrepancy arises from variation within the laboratory set-up used for fragment analysis. To obtain comparable results between laboratories using different set-ups, some form of calibration is a necessity. A simple approach is to use a set of calibration strains with known allele sizes and determine what compensation factors need to be applied under the chosen set-up conditions in order to obtain the correct allele sizes. We present here a proof-of-concept study showing that using such a set of calibration strains makes inter-laboratory comparison possible. In this study, 20 international laboratories analysed 15 test strains using a five-locus Salmonella enterica serovar Typhimurium MLVA scheme. When using compensation factors derived from a calibration set of 33 isolates, 99.4% (1,461/1,470) of the MLVA alleles of the test strains were assigned correctly, compared with 64.8% (952/1,470) without any compensation. After final analysis, 97.3% (286/294) of the test strains were assigned correct MLVA profiles. We therefore recommend this concept for obtaining comparable MLVA results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it