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Interventions for the prevention of postoperative atrial fibrillation in adult patients undergoing noncardiac thoracic surgery

2017· article· en· W1891808123 on OpenAlexaff
Sadeesh Srinathan, Richard Whitlock, Mark D Forsyth, Elizabeth R Berg, Tyler C Burnside, Tania Gottschalk

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster UniversityUniversity of Manitoba
Fundersnot available
KeywordsMedicineAtrial fibrillationCardiothoracic surgerySurgeryPsychological interventionAnesthesiaCardiac surgeryCardiology

Abstract

fetched live from OpenAlex

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: 1. To determine if adults undergoing noncardiac thoracic surgery who receive a prophylactic intervention have a lower incidence of postoperative atrial fibrillation (AF) than patients who do not receive a prophylactic intervention. 2. To determine if there are adverse events associated with the use of these prophylactic interventions. Specifically we will determine if there are differences in the incidence of strokes, ventricular arrhythmias and hypotension. For the purpose of this review, prophylactic interventions are new interventions administered to patients undergoing noncardiac thoracic surgery for the purpose of reducing the incidence of AF in those patients who are initially in sinus rhythm. The interventions are grouped into the following classes of intervention: A) cardiovascular agents, B) elemental supplementation, C) anti‐inflammatory agents. These interventions are to be administered either in the preoperative period, during the operation, or immediately at the end of the operation. We will not consider maintaining anti‐arrhythmic medication in patients who are already receiving the medication as a prophylactic intervention.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.059
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0210.002

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 head0.106
GPT teacher head0.406
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2017
Admission routes1
Has abstractyes

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