Fourier transform infrared spectroscopy as a new tool to investigate Clostridioides difficile outbreaks: A proof-of-concept study
Bibliographic record
Abstract
Whole genome sequencing (WGS), used as the main method for outbreak investigations, requires substantial technical expertise and is routinely done by reference laboratories. Therefore, the actionable information is often delayed. This study is the first to assess Fourier Transform Infrared (FTIR) spectroscopy as an alternative tool to investigate nosocomial Clostridioides difficile transmission. The FTIR spectroscopy parameters, such as C. difficile growth conditions, FTIR spectroscopy settings and data analysis, were optimized using clonally related C. difficile isolates (n = 5) and epidemiologically unlinked isolates (n = 7). The utility of FTIR to identify clonal relatedness was evaluated using C. difficile isolates (n = 9) from suspected nosocomial transmission events at different hospitals. FTIR spectroscopy results were compared to WGS single nucleotide polymorphism (SNP) analysis. The optimized FTIR protocol correctly clustered clonally related isolates, with clear separation from outgroup isolates. Three C. difficile isolates from an outbreak were identified as one cluster by FTIR whereas 2 isolates from another outbreak were not related based on FTIR results. WGS results corroborated FTIR results in both cases. Additionally, four isolates from suspected patient-to-patient transmission were found to be unrelated by WGS, whereas clustering between some of these isolates was observed by FTIR. FTIR spectroscopy demonstrated good agreement and a high negative predictive value when compared with WGS result. The positive predictive value was lower due to one false positive FTIR cluster of unrelated C. difficile isolates. This proof-of-concept study demonstrated that FTIR spectroscopy is a promising tool for C. difficile outbreak investigations and might be useful to rule-out patient-to-patient transmission.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".