MétaCan
Menu
Back to cohort
Record W4313204333 · doi:10.1016/j.cjcpc.2022.12.004

Late Palpitations in Young Patients After Ablation for Tachyarrhythmias

2022· article· en· W4313204333 on OpenAlexaff
Christopher O.Y. Li, Dania Kallas, Lucy LePoidevin, Amelia Hart, Sonia Franciosi, Shubhayan Sanatani

Bibliographic record

VenueCJC Pediatric and Congenital Heart Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsPalpitationsMedicineAblationCatheter ablationInternal medicineCardiologyPediatrics

Abstract

fetched live from OpenAlex

Background: Patients after ablation for tachyarrhythmias may continue to experience palpitations in the setting of sinus rhythm. The objective of our study was to investigate if patients who have undergone ablation for tachyarrhythmia have palpitations and other somatic complaints more frequently than healthy controls. Methods: Paediatric patients after ablation for tachyarrhythmia at BC Children's Hospital from 2009 to 2020 and healthy controls were invited to participate in a survey about palpitations. Demographics, palpitation symptoms, frequency, duration, and need for medical attention were collected and compared between patients and controls. Results: = 0.001) compared to controls. Conclusion: The prevalence of palpitations did not differ in ablation patients compared to healthy controls. Patients reported that their palpitations felt different after ablation and were more likely to seek medical attention for their palpitations. Paediatric patients with tachyarrhythmias may have heightened awareness due to their history. Clinicians can incorporate this into procedural counselling to reduce patient concern and need for medical attention.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0030.000

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCJC Pediatric and Congenital Heart DiseaseSame topicCardiac Arrhythmias and TreatmentsFrench-language works237,207