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Record W1969514468 · doi:10.1159/000129093

Acute Hypercalcemia and Increased Work Load in Canine Transplanted Heart

2008· article· en· W1969514468 on OpenAlexaff
Louis Dumont, C Chartrand

Bibliographic record

VenueEuropean Surgical Research · 2008
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
Fundersnot available
KeywordsMedicineTransplantationHemodynamicsHeart transplantationCoronary vasodilatorBolus (digestion)Blood flowCardiologyCardiac indexDiltiazemInternal medicineCardiac outputAnesthesiaCalcium

Abstract

fetched live from OpenAlex

Coronary vasodilator adjustments following cardiac transplantation might be adversely affected during severe rejection. We studied the coronary blood flow response following intravenous bolus administration of calcium (0.04-0.05 mEq/kg) in canine cardiac transplants. Fifteen dogs were submitted to orthotopic heart transplantation and equipped with electronic implants for monitoring of hemodynamic parameters. Of these animals, 9 were not immunosuppressed, while 6 were treated with ciclosporin, azathioprine, and prednisone. The effects of calcium administration upon cardiac function were evaluated during the postoperative period, at recovery (2-3 days after transplantation) and during severe rejection (7-10 days after transplantation), and also in immunosuppressed animals. Rapid calcium administration elicits brief increases in arterial pressure, cardiac index, stroke work, and coronary blood flow. There were significant differences in these effects, depending on the hemodynamic status of these animals. Coronary blood flow was significantly increased in all experiments, except when severe rejection was evidenced. These results indicate that changes in myocardial metabolic demand (work load) are not adequately matched with coronary blood flow adjustments in the presence of severe rejection.

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 score0.082
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.130
GPT teacher head0.413
Teacher spread0.283 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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