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Record W2520452469 · doi:10.1117/1.jbo.21.9.097003

Reproducibility, interchangeability of measures, time to measure stabilization, and reference values of two tissue oximeters in healthy volunteers

2016· article· en· W2520452469 on OpenAlexafffund
Alexis Cournoyer, André Denault, Sylvie Cossette, Annik Fortier, Raoul Daoust, Massimiliano Iseppon, Jean‐Marc Chauny, Éric Notebaert

Bibliographic record

VenueJournal of Biomedical Optics · 2016
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsUniversité de Montréal
FundersUniversité de Montréal
KeywordsReproducibilityIntraclass correlationMedicineInterchangeabilityConfidence intervalRepeatabilityNuclear medicineBiomedical engineeringInternal medicineMathematicsStatistics

Abstract

fetched live from OpenAlex

This study aimed to compare two tissue oximeters, the INVOS 5100c and the Equanox 7600, in terms of their reproducibility and the interchangeability of their measures. In a randomized order, three measurements were taken at six different sites on both sides of the body in 53 healthy volunteers. Intraclass correlation coefficients (ICC) and within-subject standard deviation (Sw) were calculated for each device. The ICCs were compared using Fisher r-to-z transformation and the Sw were compared using paired-sample t-tests. We found no difference between the reproducibility of the INVOS {ICC=0.92 [95% confidence interval (CI) 0.90 to 0.93]} and Equanox [ICC=0.90 (95% CI 0.88 to 0.93)] in terms of ICCs (p=0.06). However, the Equanox [Sw=1.96 (95% CI 1.91 to 2.02)] showed a better Sw than the INVOS [Sw=2.11 (95% CI 2.05 to 2.17)] (p=0.019). Also, when compared directly to stable condition, the readings produced by the two oximeters varied considerably [ICC 0.43 (95% CI 0.36 to 0.49)]. When taken individually, both tissue oximeters displayed good reproducibility, the Equanox being slightly better than the INVOS in terms of absolute reproducibility. However, when compared, the oximeters showed poor interdevices agreement. Reference values were also described.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.357
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.341
Teacher spread0.296 · 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 teacher head, 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

Citations8
Published2016
Admission routes2
Has abstractyes

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