Aging effects on QT interval: Implications for cardiac safety of antipsychotic drugs.
Bibliographic record
Abstract
OBJECTIVES: To explore the effect of aging on cardiac toxicity specifically the interaction of age and antipsychotic drugs to alter the QT interval. METHODS: THE MEDLINE DATABASES WERE SEARCHED USING THE OVIDSP PLATFORMS WITH THE SEARCH STRATEGY: "QT interval" or "QT" and "age" or "aging". The entry criteria were: over 10,000 apparently healthy individuals with data on both sexes; QT interval corrected for heart rate (QTc) and an expression of its variance for multiple age decades extending into the older ages. RESULTS: QTc increased in duration with increasing age. Considering a modest one SD increment in QTc in the normal population, the addition of Chlorpromazine produced a QTc on average greater than 450 ms for ages 70 years and older. Risperidone, that did not on average alter QTc, would be expected to produce a QTc of 450 ms in persons in their mid 70 years under some circumstances. QTc prolongation > 500 ms with antipsychotic drugs is more likely for persons with QTc initially at the 99(th) percentile. It may occur with Haloperidol which does not on average alter QTc. CONCLUSIONS: The range of values for the QT interval in apparently normal older men or women, when combined with the range of expected QT interval changes induced by antipsychotic drugs, can readily be associated with prolonged QTc. Individuals with QTc at the 99(th) percentile may have serious QTc prolongation with antipsychotic drugs even those that are not usually associated with QTc prolongation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".