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Record W4229000362 · doi:10.1016/j.cjcpc.2022.04.003

The Critical Transfer From Paediatrics to Adult Care in Patients With Congenital Heart Disease: Predictors of Transfer and Retention of Care

2022· article· en· W4229000362 on OpenAlexaff
Asem Suliman, Ruochen Mao, Brett Hiebert, James W. Tam, Ashish H. Shah, Reeni Soni, Robin Ducas

Bibliographic record

VenueCJC Pediatric and Congenital Heart Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of ManitobaMcMaster University
Fundersnot available
KeywordsMedicineReferralAttendanceHeart diseasePsychological interventionPediatricsRetrospective cohort studyEmergency medicineIntensive care medicineFamily medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

Background: Congenital heart disease is the most common congenital birth defect and presents with differing degrees of complexity. Patients require lifelong specialized care. The transfer from paediatric to adult care is a time of risk that may result in lapses or loss of care. A successful transfer from paediatric to adult care is integral for improved patient outcomes. Methods: In this retrospective study, we used the paediatric cardiology database and the electronic records at the adult congenital heart disease (ACHD) clinic to identify referrals and successful transfer between 2008 and 2017. Successful transfer was defined as a patient referred to the ACHD clinic who was seen in the clinic and has ongoing follow-up. We also sought to identify predictors of a successful transfer. Results: A total of 555 patients were referred to the ACHD clinic (2008-2017). Of all patients referred, 62% had a successful transfer and an ongoing specialist care. The remaining 38% either did not show for first appointments or missed 3 consecutive visits. Independent predictors of a successful transfer were the presence of moderate or complex ACHD, residing within the city limits, older age at the time of referral, and a more recent year of referral. Conclusions: Over one-third of patients did not achieve successful transfer, namely attendance at first clinic visit plus early retention in care. We were able to identify several variables that predict successful transfer. Further research is required to identify interventions that can be implemented to reduce lapses in patient care.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.223
Teacher spread0.217 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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