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Record W2046277472 · doi:10.1117/12.831611

Tissue oxygenation during exercise measured with NIRS: a quality control study

2009· article· en· W2046277472 on OpenAlexaff
Erwin Gerz, Dmitri Geraskin, J. Patrick Neary, Julia Franke, Petra Platen, Matthias Kohl‐Bareis

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsReproducibilityBiomedical engineeringStandard deviationOxygenationMaterials scienceMathematicsMedicineStatisticsAnesthesia

Abstract

fetched live from OpenAlex

We assess the data quality of calculated tissue oxygen saturation (SO2) and haemoglobin concentrations recorded on muscle during an incremental cycling protocol in healthy volunteers. The protocol was repeated three times at the same day and a fourth time at a different day to estimate the reproducibility of the method. A novel broad-band, spatially resolved spectrometer (SRS) system was employed which allowed us to compare SRS-based oxygenation parameters with modified Lambert-Beer (MLB) data. We found that the inter-subject variation in SO<sub>2</sub> (standard deviation about 6 %) is considerably larger than the reproducibility (about 1.5 %) both for same day and different day tests. When changes in SO<sub>2</sub> during the cycling test were considered the reproducibility is better than 1 %. Time courses of SRS-based haemoglobin parameters are different from MLB-data with higher reproducibility for SRS. The magnitudes of the haemoglobin changes were found to be considerably larger for the SRS method. Furthermore, the broad band approach was tested against a four-wavelength analysis with the differences found to be negligible.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.279
Teacher spread0.266 · 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.

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

Citations2
Published2009
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Imaging and Spectroscopy TechniquesFrench-language works237,207