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Abstract 20897: Patient-Reported Outcomes in Tetralogy of Fallot: Baseline Results From a Prospective, International, Multi-Site Study

2017· article· en· W2908608364 on OpenAlexaff
Rachel M. Wald, Michael E. Farkouh, Christopher A. Caldarone, Nagib Dahdah, Frédéric Dallaire, Christian Drolet, Jasmine Grewal, Edward Hickey, Camilla Kayedpour, Paul Khairy, Benedetta Leonardi, Brian W. McCrindle, Syed Najaf Nadeem, Ming‐Yen Ng, Erwin Oechslin, Andrew N. Redington, Candice K. Silversides, Edythe Tham, Judith Therrien, Isabelle Vonder Muhll, Andrew E. Warren, Bernd J. Wintersperger, Adrienne H. Kovacs

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsJewish General HospitalMontreal Heart InstituteCentre Hospitalier Universitaire de SherbrookeIzaak Walton Killam Health CentreStollery Children's HospitalSt. Paul's HospitalCentre Hospitalier Universitaire Sainte-JustineCanadian VIGOUR CentreQueen Elizabeth II Health Sciences CentreUniversité LavalHospital for Sick ChildrenUniversity Health Network
Fundersnot available
KeywordsMedicineQuality of life (healthcare)AnxietyDepression (economics)Tetralogy of FallotProspective cohort studyMental healthInternal medicinePhysical therapyGerontologyPsychiatryHeart disease

Abstract

fetched live from OpenAlex

Introduction: In addition to understanding causes of morbidity and mortality following tetralogy of Fallot repair (rTOF), the importance of patient-reported outcomes (PROs) such as quality of life (QOL) is increasingly being recognized. Hypothesis: We hypothesized that PROs in rTOF would be associated with selected sociodemographic factors, functional status, and/or clinical variables. Methods: As part of a prospective study of patients ≥12 years with rTOF and significant pulmonary regurgitation, participants completed PRO surveys: SF-12 Health Status (physical component summary [PCS] and mental component summary [MCS] t-scores), EQ-5D (5 dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), and a 0-100 QOL linear analogue scale (QOL-LAS). With multivariable regression analysis, we determined the association between PROs and selected predictors. Results: We studied n=627 participants (55% male, 26±13 years, 75% adult) at 14 sites (North America, Europe and Asia). Median age at repair was 1.6 years (IQR 0.6, 4.2). In general, scores on the SF-12 and QOL-LAS suggested good outcomes: SF-12 PCS <18 years 52±6 and ≥18 years 51±9; SF-12 MCS <18 years 52±9 and ≥18 years 49±10; QOL-LAS <18 years 83±14 and ≥18 years 78±16. On the EQ-5D, difficulties were reported with usual activities (16%), pain (25%) and anxiety/depression (38%). In multivariable regression analysis, younger age at enrollment emerged as an independent predictor of better physical health status and worse mental health status (p=0.0005 and p=0.029, respectively), but not QOL. Better NYHA functional class was a predictor of better SF-12 PCS and MCS (p<0.0005 and p=0.002) as well as better QOL (p<0.0001). Other factors (including complexity of underlying TOF anatomy, previous shunt palliation and need for arrhythmia intervention) were not associated with PROs. Conclusions: Age and NYHA functional class at enrollment were found to be predictors of PROs in rTOF. While global measures of health status and QOL (SF-12 and QOL-LAS) suggest relatively good PROs, this study demonstrates the importance of inquiring about specific health problems (EQ-5D) to achieve a richer understanding of PROs in order to potentially maximize patient well-being.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.342
Teacher spread0.294 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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