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Record W4377967617 · doi:10.1177/13674935231174503

Multidisciplinary coordination of care for children with esophageal atresia and tracheoesophageal fistula

2023· article· en· W4377967617 on OpenAlexaff
J Platt, Alberto Nettel‐Aguirre, Candice Bjornson, Ian Mitchell, Kathryn A. Davis, JA Michelle Bailey

Bibliographic record

VenueJournal of Child Health Care · 2023
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsAlberta Children's HospitalAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsMedicineTracheoesophageal fistulaCohortMultidisciplinary approachAtresiaRetrospective cohort studyEmergency departmentOutpatient clinicPediatricsEmergency medicineFamily medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

Esophageal Atresia/Tracheoesophageal Atresia (EA/TEF) is a multisystem congenital anomaly. Historically, children with EA/TEF lack coordinated care. A multidisciplinary clinic was established in 2005 to provide coordinated care and improve access to outpatient care. This single-center retrospective cohort study was conducted to describe our cohort of patients with EA/TEF born between March 2005 and March 2011, assess coordination of care, and to compare outcomes of children in the multidisciplinary clinic to the previous cohort without a multi-disciplinary clinic. A chart review identified demographics, hospitalizations, emergency visits, clinic visits, and coordination of outpatient care. Twenty-seven patients were included; 75.9% had a C-type EA/TEF. Clinics provided multidisciplinary care and compliance with the visit schedule was high with a median of 100% (IQR 50). Compared to the earlier cohort, the new cohort ( N = 27) had fewer hospital admissions and LOS was reduced significantly in the first 2 years of life. Multidisciplinary care clinics for medically complex children can improve coordination of visits with multiple health care providers and may contribute to reduced use of acute care services.

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 score0.092
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.311
Teacher spread0.302 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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