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Record W2108091297 · doi:10.1016/j.ejcts.2008.09.020

A method to distinguish between gaseous and solid cerebral emboli in patients with prosthetic heart valves

2008· article· en· W2108091297 on OpenAlexaff
R Rodriguez, Howard J. Nathan, Marc Ruel, Fraser D. Rubens, David Dafoe, Thierry Mesana

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTranscranial DopplerHeart valveMedicineIntensity (physics)Mechanical heart-valveOxygenUltrasonic sensorBiomedical engineeringCardiologyRadiologyChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: The difficulty of distinguishing solid from air emboli using transcranial Doppler has limited its use in situations where both types of emboli can occur, such as in mechanical heart valve patients. To make transcranial Doppler clinically useful, a method must be found to distinguish benign air bubbles from the more damaging solid particulates. Since inhalation of 100% oxygen reduces the amount of air bubbles in mechanical heart valve patients, the ultrasonic features of the remaining emboli would be characteristic of solid particulates. OBJECTIVE: We determined the accuracy of the signal relative intensity measured with transcranial Doppler to distinguish between gaseous and non-gaseous emboli in mechanical heart valve patients examined during room air and 100% oxygen. Embolic signals detected in patients with bioprosthetic valves examined during 100% oxygen comprised the source of solid particulates. METHODS: Embolic signals were detected during room air (n=141) and 100% oxygen (n=45) from 17 mechanical valve patients at two Doppler examinations (4h and 4 days after surgery). Solid embolic signals (n=31) from seven patients with bioprosthetic valves were identified with 100% oxygen within the first 4h after surgery. Frequency plots and receiver operating characteristic curves assessed signal intensity differences between mechanical and bioprosthetic valve groups during 100% oxygen and the efficacy of the relative intensity for differentiating gaseous from solid emboli. RESULTS: Administration of 100% oxygen during transcranial Doppler examination in mechanical heart valve patients decreased the count of embolic signals compared with room air (p=0.006). The embolic signals of mechanical heart valve patients breathing 100% oxygen showed lower relative intensities compared with those during room air. The distribution of the signal relative intensity between mechanical and bioprosthetic valve groups during 100% oxygen was similar. A 16dB cut-off threshold achieved the best accuracy for differentiating non-gaseous from gaseous emboli (sensitivity: 60%; specificity: 82%; area: 0.721; p<0.0001). CONCLUSIONS: The use of a signal intensity cut-off offers adequate discrimination of the embolic composition in mechanical heart valve patients. Future studies evaluating prophylactic treatments of thrombosis in these patients should assess the predictive value of this intensity threshold and their potential association with outcome indicators and procoagulant markers.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.024
GPT teacher head0.284
Teacher spread0.259 · 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 designObservational
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

Citations18
Published2008
Admission routes1
Has abstractyes

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