Bibliographic record
Abstract
PURPOSE: Mutations in KCNQ1 are linked to long QT and other syndromes. This study reports a method to predict clinical outcome when a mutation at KCNQ1 is found. METHODS: We used amino-acid distribution probability to measure KCNQ1 mutants, and cross-impact analysis to couple KCNQ1 mutants with clinical outcome. Then, Bayesian equation was used to calculate the probability of occurrence of long-QT syndrome in the presence of a mutation. RESULTS: Seventy-six mutations were classified into two groups according to whether a mutation increased or decreased amino-acid distribution probability. Cross-impact analysis showed that a mutation that increases the distribution probability has a greater chance of causing long-QT syndrome than a mutation that decreases the distribution probability. Bayesian calculation suggested that a patient would have a 90% chance of developing long-QT syndrome when a mutation is found at KCNQ1. CONCLUSION: This study details the process of building a quantitative relationship between KCNQ1 mutations and clinical outcome and provides the probability of LQT1 in the presence of a mutation.
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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 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".