Comparative analysis of Gram's stain, <scp>PNA</scp>‐<scp>FISH</scp> and Sepsityper with <scp>MALDI</scp>‐<scp>TOF MS</scp> for the identification of yeast direct from positive blood cultures
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
Fungaemia diagnosis could be improved by reducing the time to identification of yeast from blood cultures. This study aimed to evaluate three rapid methods for the identification of yeast direct from blood cultures; Gram's stain analysis, the AdvanDX Peptide Nucleic Acid in Situ Hybridisation Yeast Traffic Light system (PNA-FISH YTL) and Bruker Sepsityper alongside matrix-assisted laser desorption ionisation time of flight mass spectrometry (MALDI-TOF MS). Fifty blood cultures spiked with a known single yeast strain were analysed by blinded operators experienced in each method. Identifications were compared with MALDI-TOF MS CHROMagar Candida culture and ITS rRNA sequence-based identifications. On first attempt, success rates of 96% (48/50) and 76% (36/50) were achieved using PNA-FISH YTL and Gram's stain respectively. MALDI-TOF MS demonstrated a success rate of 56% (28/50) when applying manufacturer's species log score thresholds and 76% (38/50) using in-house parameters, including lowering the species log score threshold to >1.5. In conclusion, PNA-FISH YTL demonstrated a high success rate successfully identifying yeast commonly encountered in fungaemia. Sepsityper(™) with MALDI-TOF MS was accurate but increased sensitivity is required. Due to the misidentification of commonly encountered yeast Gram's stain analysis demonstrated limited utility in this setting.
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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 | 0.004 |
| 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 | 0.000 |
| 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