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Record W2018574421 · doi:10.1039/c0an00996b

Quantitative impact of small angle forward scatter on whole blood oximetry using a Beer–Lambert absorbance model

2011· article· en· W2018574421 on OpenAlexafffund
Serge Emile LeBlanc, Monica Atanya, Kevin D. Burns, Réjean Munger

Bibliographic record

VenueThe Analyst · 2011
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsUniversity of OttawaOttawa Hospital
FundersUniversity of Ottawa
KeywordsAbsorbanceBeer–Lambert lawOpticsPhysics

Abstract

fetched live from OpenAlex

It is well known that red blood cell scattering has an impact on whole blood oximetry as well as in vivo retinal oxygen saturation measurements. The goal of this study was to quantify the impact of small angle forward scatter on whole blood oximetry for scattering angles found in retinal oximetry light paths. Transmittance spectra of whole blood were measured in two different experimental setups: one that included small angle scatter in the transmitted signal and one that measured the transmitted signal only, at absorbance path lengths of 25, 50, 100, 250 and 500 µm. Oxygen saturation was determined by multiple linear regression in the 520-600 nm wavelength range and compared between path lengths and experimental setups. Mean calculated oxygen saturation differences between setups were greater than 10% at every absorbance path length. The deviations to the Beer-Lambert absorbance model had different spectral dependences between experimental setups, with the highest deviations found in the 520-540 nm range when scatter was added to the transmitted signal. These results are consistent with other models of forward scatter that predict different spectral dependences of the red blood cell scattering cross-section and haemoglobin extinction coefficients in this wavelength range.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.140
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.064
GPT teacher head0.275
Teacher spread0.211 · 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

Citations6
Published2011
Admission routes2
Has abstractyes

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