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Record W2096625632 · doi:10.1117/1.jbo.17.9.097005

On the noninvasive optical monitoring and differentiation of methemoglobinemia and sulfhemoglobinemia

2012· article· en· W2096625632 on OpenAlexafffund
Gladimir V. G. Baranoski, Tenn F. Chen, Bradley W. Kimmel, Erik Miranda, Daniel Yim

Bibliographic record

VenueJournal of Biomedical Optics · 2012
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMethemoglobinemiaIn silicoMethemoglobinComputer scienceMedicineBiologyInternal medicineHemoglobinGenetics

Abstract

fetched live from OpenAlex

There are several pathologies whose study and diagnosis is impaired by a relatively small number of documented cases. A practical approach to overcome this obstacle and advance the research in this area consists in employing computer simulations to perform controlled in silico experiments. The results of these experiments, in turn, may be incorporated in the design of differential protocols for these pathologies. Accordingly, in this paper, we investigate the spectral responses of human skin affected by the presence of abnormal amounts of two dysfunctional hemoglobins, methemoglobin and sulfhemoglobin, which are associated with two life-threatening medical conditions, methemoglobinemia and sulfhemoglobinemia, respectively. We analyze the results of our in silico experiments and discuss their potential applications to the development of more effective noninvasive monitoring and differentiation procedures for these medical conditions.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.304
Teacher spread0.288 · 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

Citations26
Published2012
Admission routes2
Has abstractyes

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