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Record W2130678340 · doi:10.1017/s026502150400033x

Cardiopulmonary bypass induces significant platelet activation in children undergoing open-heart surgery

2004· article· en· W2130678340 on OpenAlexaff
Joanne Guay, Pierre Ruest, Louise Lortie

Bibliographic record

VenueEuropean Journal of Anaesthesiology · 2004
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineCardiopulmonary bypassPlateletCardiac surgeryHeart diseaseAnesthesiaPlatelet activationVenous bloodCardiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: To evaluate the effects of cardiopulmonary bypass (CPB) on platelet function in children undergoing open-heart surgery. METHODS: Data from 16 consecutive children undergoing cardiac surgery with CPB were prospectively collected. Blood samples of 10 mL were collected via the central venous line immediately before and after CPB for CD62 measurements by flow cytometry. RESULTS: Ten children had acyanotic heart disease (median age 3 yr, range 1.8-14) and six had a cyanotic defect (median age 4yr, range 2-14). The platelet count decreased significantly with CPB in both groups: from 163.5 (130-201) to 93.5 (57-186) x 10(3) microL(-1) in acyanotic children and from 139.5 (77-212) to 75 (43-99) x 10(3) microL(-1) in cyanotic children (P < 0.0001). The percentage of activated platelets was significantly lower in acyanotic children at baseline: 1% (0-23%) vs. 5% (3-8%) (P = 0.07). CPB increased the percentage of activated platelets significantly in both groups: post-bypass the values were 10% (range 1-17%) in acyanotic children and 7% (range 1-30%) in cyanotic children (P = 0.03). The increase in the percentage of activated platelets did not differ between the two study groups (P = 0.11). CONCLUSION: CPB induces significant platelet activation in children undergoing open-heart surgery.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.024
GPT teacher head0.257
Teacher spread0.234 · 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

Citations20
Published2004
Admission routes1
Has abstractyes

Explore more

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