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Record W2143563639 · doi:10.1093/europace/eul123

Influence of gender on ICD implantation for primary and secondary prevention of sudden cardiac death

2006· article· en· W2143563639 on OpenAlexaff
Darryl R. Davis, Anthony Tang, Robert Lemery, Martin S. Green, Michael H. Gollob, David H. Birnie

Bibliographic record

VenueEP Europace · 2006
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineShock (circulatory)Secondary preventionInternal medicineCardiologySudden cardiac deathPrimary preventionImplantable cardioverter-defibrillatorDisease

Abstract

fetched live from OpenAlex

AIMS: This study sought to investigate the influence of gender on access to ICD therapy and examine the influence of gender on subsequent ICD shock experience. METHODS AND RESULTS: The records of 353 consecutive patients (140 and 213 secondary prevention, respectively) who received their first ICD between January 2000 and March 2004 were reviewed. All patients fulfilled criteria for primary or secondary prevention ICD implantation. Baseline characteristics and ICD shock experiences were compared. Female patients were younger and less likely to have a history of ischaemic heart disease or atrial arrhythmias (P<0.01). In contrast, female patients were more likely to have heart failure and diabetes (P<0.01). Markedly fewer females received an ICD for either primary (M:F ratio 8.5:1, P<0.01) or secondary (M:F ratio 4.5:1, P<0.01) prevention. Further, significantly fewer female patients received an ICD for MADIT II indications (M:F 11.2:1, P<0.01). Over the mean follow-up of 1.8+/-1.1 years, gender had no influence upon the likelihood of receiving either an appropriate or an inappropriate shock (P=ns). CONCLUSION: Although male patients accounted for the great majority (85%) of all ICD recipients, there was no evidence of influence of gender on the likelihood of receiving an appropriate or inappropriate shock.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.290
Teacher spread0.272 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

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