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Record W2023942515 · doi:10.1097/aco.0000000000000027

Cerebral oximetry and thoracic surgery

2013· review· en· W2023942515 on OpenAlexaff
Inderveer Mahal, Sophie N. Davie, Hilary P. Grocott

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2013
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Boniface HospitalUniversity of Manitoba
Fundersnot available
KeywordsMedicineCardiothoracic surgeryPerioperativeIntensive care medicineCardiac surgeryModalitiesHypoxemiaAnesthesiaSurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Cerebral oximetry, though first described for clinical use in cardiac surgery, has been increasingly used in the setting of thoracic surgery. Research focusing on the use of cerebral oximetry in this setting is relatively sparse. This review outlines our current understanding of the use of cerebral oximetry for thoracic surgery. RECENT FINDINGS: Cerebral desaturation, though variably defined, is a relatively common occurrence during thoracic surgery. The reasons for this desaturation largely relate to perioperative hypoxemia, but may also be related to other physiologic disturbances such as lateral decubitus positioning, one-lung ventilation, as well as other nonhypoxemia-related mechanisms. It is unlikely to result from specific reductions in cardiac output. There is some preliminary data suggesting a relationship between cerebral desaturation and various adverse postoperative outcomes. SUMMARY: Although it is clear that cerebral desaturation can commonly occur during thoracic surgery, it is partly dependent upon how desaturation is defined. The relationship between cerebral desaturation and adverse outcomes after thoracic surgery, as well as the potential ability for cerebral oximetry to guide therapeutic modalities, awaits much needed additional research before being more widely accepted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.212
GPT teacher head0.435
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2013
Admission routes1
Has abstractyes

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