Evaluation of three <scp>MALDI</scp>‐<scp>TOF</scp> mass spectrometry libraries for the identification of filamentous fungi in three clinical microbiology laboratories in Manitoba, Canada
Bibliographic record
Abstract
Matrix-assisted laser desorption ionisation-time of flight mass spectrometry (MALDI-TOF MS) is commonly used by clinical microbiology laboratories to identify bacterial pathogens and yeasts, but not for the identification of moulds. Recent progress in extraction protocols and the composition of comparative libraries support potential application of MALDI-TOF MS for mould identification in clinical microbiology laboratories. We evaluated the performance of the Bruker Microflex™ MALDI-TOF MS instrument (Billerica, MA, USA) to identify clinical isolates and reference strains of moulds using 3 libraries, the Bruker mould library, the National Institutes of Health (NIH) library and the Mass Spectrometry Identification (MSI) online library, and compared those results to conventional (morphological) and molecular (18S/ITS; gold standard) identification methods. All 3 libraries demonstrated greater accuracy in genus identification (≥94.9%) than conventional methods (86.4%). MALDI-TOF MS identified 73.3% of isolates to species level compared to only 31.7% by conventional methods. The MSI library demonstrated the highest rate of species-level identification (72.0%) compared to NIH (19.5%) and Bruker (13.6%) libraries. Greater than 20% of moulds remained unidentified to species level by all 3 MALDI-TOF MS libraries primarily because of library limitations or imperfect spectra. The overall identification rate of each MALDI-TOF MS library depended on the number of species and the number of spectra representing each species in the library.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".