Addressing a dual public health emergency: supporting physicians to prescribe opioid medications
Bibliographic record
Abstract
Aims Carbapenem resistance in Bacteroides fragilis is emerging and is mainly attributed to insertion sequence (IS)-mediated activation of the carbapenemase gene cfiA. We investigated the use of matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and the CarbaNP assay for the rapid identification of these strains. Methods This study used the Bruker MALDI Biotype system and the mass spectra models generated by 20 B. fragilis reference strains (10 cfiA-positive and 10 cfiA-negative) in the ClinProTools software to identify 404 B. fragilis (71 cfiA-positive and 333 cfiA-negative) clinical isolates. The ability of the CarbaNP assay to detect IS-mediated activation of the cfiA gene was assessed and the results obtained by molecular analysis were used as reference methods. Results The support vector machine model generated by ClinProTools was found to be the most reliable algorithm for differentiation of cfiA-positive and cfiA-negative B. fragilis subgroups. Using the direct transfer method, all but one cfiA-negative isolates were correctly identified to the two subgroups by the model. The correct identification of the cfiA-negative isolate was obtained upon retesting by the extraction method. Of the 81 cfiA-positive isolates, PCR and sequencing showed that 30 had an IS element providing the promoter for activation of cfiA. With regard to the presence of the IS element, the CarbaNP test in the cfiA upstream region had 100% sensitivity, 80.4% specificity, 75.0% positive predictive value and 100% negative predictive value. Conclusions The combination of MALDI-TOF MS and the CarbaNP assay can be applied in diagnostic clinical laboratory for rapid identification of B. fragilis with IS element-activated cfiA gene.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".