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Record W2099283213 · doi:10.1177/108925320200600306

Initial Perioperative Care of the Cardiac Surgical Patient

2002· article· en· W2099283213 on OpenAlexaff
Daniel Bainbridge, Davy Cheng

Bibliographic record

VenueSeminars in Cardiothoracic and Vascular Anesthesia · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSt Joseph's Health CareLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineAnesthesiologyCardiac surgeryPerioperativeIntensive care unitAnesthesiaIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Recently, changes in the management of cardiac patients have allowed earlier discharge from the cardiac recovery area and reduced hospital length of stay. These changes have been drien by a need to reduce the cost of cardiac surgery and imrove efficiency. This change has been both financially sucessful and safe for patients. To allow for this success, a joint effort is required between the departments of cardiac surgery and anesthesiology involving the preoperative, intraoperative and postoperative treatment of these patients. Through recogition of suitable candidates, modifications in anesthetic techique, and appropriate postoperative management, the goal of extubation within 6 hours of admission to the cardiac recovery area can be achieved. Changes in intraoperative and early postoperative management of cardiac surgical patients are discussed. Specific recovery models are reviewed with disussion of the parallel and integrated models. Methods of preicting prolonged extubation times and intensive care unit length of stay are also discussed. Initial management of the cardiac patient in the cardiac recovery area is presented with a more in-depth review of specific complications: stroke, atril fibrillation, blood loss, left ventricular dysfunction, and pulonary dysfunction.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

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