MétaCan
Menu
Back to cohort
Record W3205863181 · doi:10.1128/jcm.01398-21

Multicenter Evaluation of Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) Spectroscopy-Based Method for Rapid Identification of Clinically Relevant Yeasts

2021· article· en· W3205863181 on OpenAlexafffund
Lisa M. T. Lam, Ashraf A. Ismail, Simon Lévesque, Simon F. Dufresne, Mathew P. Cheng, Émilie Vallières, Me‐Linh Luong, Jacqueline Sedman, Philippe J. Dufresne

Bibliographic record

VenueJournal of Clinical Microbiology · 2021
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsHôpital Maisonneuve-RosemontSte. Anne's HospitalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier de l’Université de MontréalMcGill UniversityCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanMcGill University Health CentreCentre Hospitalier Universitaire de SherbrookeCentre Hospitalier Universitaire Sainte-JustineUniversité de SherbrookeUniversité Sainte-Anne
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsAttenuated total reflectionFourier transform infrared spectroscopyInfraredFourier transformMulticenter studyFourier transform spectroscopyInfrared spectroscopySpectroscopyMedicineChemistryPathologyOpticsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

= 24) isolates were obtained for evaluation and resulted in 100% correct identification. Overall, the data obtained in our multicenter evaluation study using multiple spectrometers and system operators indicate that ATR-FTIR spectroscopy is a reliable, cost-effective yeast identification technique that provides accurate and timely (∼3 min/sample) species identification promptly after the initial culture.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.006
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.045
GPT teacher head0.393
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical MicrobiologySame topicBiosensors and Analytical DetectionFrench-language works237,207