What is the Optimal Age for Repair of Tetralogy of Fallot?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".