Quality evaluation of LC‐MS/MS‐based <i>E. coli</i> H antigen typing (MS‐H) through label‐free quantitative data analysis in a clinical sample setup
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The need for rapid and accurate H typing is evident during Escherichia coli outbreak situations. This study explores the transition of MS-H, a method originally developed for rapid H antigen typing of E. coli using LC-MS/MS of flagella digest of reference strains and some clinical strains, to E. coli isolates in clinical scenario through quantitative analysis and method validation. EXPERIMENTAL DESIGN: Motile and nonmotile strains were examined in batches to simulate clinical sample scenario. Various LC-MS/MS batch run procedures and MS-H typing rules were compared and summarized through quantitative analysis of MS-H data output for a standard method development. RESULTS: Label-free quantitative data analysis of MS-H typing was proven very useful for examining the quality of MS-H result and the effects of some sample carryovers from motile E. coli isolates. Based on this, a refined procedure and protein identification rule specific for clinical MS-H typing was established and validated. CONCLUSIONS AND CLINICAL RELEVANCE: With LC-MS/MS batch run procedure and database search parameter unique for E. coli MS-H typing, the standard procedure maintained high accuracy and specificity in clinical situations, and its potential to be used in a clinical setting was clearly established.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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