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Record W2077400345 · doi:10.1177/1089253207311685

A Proposed Algorithm for the Intraoperative Use of Cerebral Near-Infrared Spectroscopy

2007· article· en· W2077400345 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSeminars in Cardiothoracic and Vascular Anesthesia · 2007
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsWestern UniversityMontreal Heart Institute
Fundersnot available
KeywordsMedicineCerebral perfusion pressureOxygenPerfusionOxygen deliveryNasal cannulaCerebral blood volumeCannulaNear-infrared spectroscopyCerebral blood flowAnesthesiaBiomedical engineeringAlgorithmCardiologySurgeryComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Near-infrared spectroscopy (NIRS) is a technique that can be used as a noninvasive and continuous monitor of the balance between cerebral oxygen delivery and consumption. The authors develop and propose an algorithm for the use of NIRS based on optimizing factors that can affect cerebral oxygen supply/demand. These factors are the position of the vascular cannula, perfusion pressure, arterial oxygen content, partial pressure of carbon dioxide, haemoglobin, cardiac output, and the cerebral metabolic rate of oxygen. Dissemination of a useful treatment algorithm is the primary purpose of this article. Further multicenter studies are necessary to confirm the benefits and cost-effectiveness of this promising monitoring modality.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.313
Teacher spread0.297 · 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