Laser ablation of placental anastomoses in twin‐to‐twin transfusion syndrome: preoperative predictors of death by recursive partitioning
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop a simple clinical algorithm for prediction of donor and recipient death using 'yes'or 'no' questions through the process of recursive partitioning for patients undergoing laser therapy for twin to twin transfusion syndrome (TTTS). The intent was to identify a subset of patients with very high specificity to whom clinical decisions would be simplified. METHOD: Secondary analysis of data retrospectively collected from laser procedures was performed for TTTS at NAFTNet centers from 2002 to 2009. Preoperative factors associated with donor and recipient death were identified by recursive partitioning regression analysis. Classification And Regression Trees (CARTs) were developed to refine specificity for prediction of death. RESULTS: There were 466 TTTS patients from eight centers. CARTs were obtained for prediction of donor death. Improved specificity was achieved through recursive partitioning as demonstrated in receiver operator characteristic curves for prediction of death of the donor. There was less than optimal predictive ability for prediction of death in the recipient, as demonstrated by lack of generation of CARTs. CONCLUSION: Recursive partitioning improves the specificity and refines the prediction of donor fetal and neonatal demise in TTTS treated with laser therapy. This has the potential to improve therapeutic choices and refine counseling regarding outcomes.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".