Bibliographic record
Abstract
An elderly patient with multiple chronic diseases and nonspecific symptoms might present a diagnostic challenge, in part because of the risk of drug-drug and drugdisease interactions. We report a case of unexplained syncope of unexpected origin. Case report A 93-year-old woman living alone in the community was found unconscious in the bathtub. She was admitted to hospital by a cardiologist and discharged after an uneventful 24-hour stay. Two days later, she was found lying on her bedroom floor. She was taken back to the emergency department, where she was alert and able to get in and out of bed, get on and off the toilet, and walk with minimal assistance. Her heart rate varied from 48 to 60 beats a minute and cardiac rhythm was irregular. There were no focal neurologic signs. Electrocardiographic examination revealed sinus rhythm with atrial premature beats and left ventricular hypertrophy. She was re-admitted to hospital by her family physician. Her history showed three syncopal events over a 2-year period since starting donepezil for Alzheimer’stype dementia. She had heart failure from diastolic dysfunction, mild obstructive pulmonary disease, and essential hypertension. Medications included clonidine, 0.05 mg bid; furosemide, 20 mg daily; various vitamins; and salbutamol, ipratropium, and fluticasone via metered dose inhaler. Because clinical trials have reported a doubling of the risk of syncope in treated subjects, 1 donepezil was
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".