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What is the Optimal Age for Repair of Tetralogy of Fallot?

2000· article· en· W2137126315 on OpenAlexaff
Glen S. Van Arsdell, Gyaandeo S. Maharaj, J. Tom, V. Koneti Rao, John G. Coles, Robert M. Freedom, William G. Williams, Brian W. McCrindle

Bibliographic record

VenueCirculation · 2000
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsMedicineTetralogy of FallotAsymptomaticSurgeryPediatricsHeart diseaseCardiology

Abstract

fetched live from OpenAlex

BACKGROUND: Controversy regarding the timing for the repair of tetralogy of Fallot centers around initial palliation versus primary repair for the symptomatic neonate/young infant and the optimal age for repair of the asymptomatic child. We changed our approach from one of initial palliation in the infant to one of primary repair around the age of 6 months, or earlier if clinically indicated. We examined the effects of this change in protocol and age on outcomes. METHODS AND RESULTS: The records of 227 consecutive children who had repair of isolated tetralogy of Fallot from January 1993 to June 1998 were reviewed. The median age of repair by year fell from 17 to 8 months (P:<0.01). The presence of a palliative shunt at the time of repair decreased from 38% to 0% (P:<0.01). Mortality (6 deaths, 2. 6%) improved with time (P:=0.02), with no mortality since the change in protocol (late 1995/early 1996). Multivariate analysis for physiological outcomes of time to lactate clearance, ventilation hours, and length of stay, but not death, demonstrated that an age <3 months was independently associated with prolongation of times (P:<0.03). Each of the deaths occurred with primary repair at an age >12 months. The best survival and physiological outcomes were achieved with primary repair in children aged 3 to 11 months. CONCLUSIONS: On the basis of mortality and physiological outcomes, the optimal age for elective repair of tetralogy of Fallot is 3 to 11 months of age.

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 score0.463
Threshold uncertainty score0.193

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 head0.027
GPT teacher head0.299
Teacher spread0.271 · 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

Citations220
Published2000
Admission routes1
Has abstractyes

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