Clinical Reasoning: A 71-year-old man with rapidly progressive dementia
Bibliographic record
Abstract
A 71-year-old right-handed man presented to the emergency room of our institution with the chief complaint of cognitive impairment of recent onset. His medical history was notable for treated hypertension, hyperlipidemia, and depression. He noted a 3-week history of short-term memory impairment (forgetting where he parked his car, misplacing items, forgetting conversations), behavioral changes (short-tempered, apathetic, easily disoriented in unfamiliar settings), generalized fatigue, weight loss, and visual impairment (possible right homonymous hemianopsia), the last of which resolved prior to presentation. He was otherwise able to carry out his basic activities of daily living and continued to drive a car, albeit with some difficulty. Physical examination was notable for 12/30 points on the Montreal Cognitive Assessment, losing points mainly for impaired visuospatial-executive function, attention, and delayed recall. He was only mildly disoriented and could not name the hospital or the day of the week. His affect was flat and he appeared apathetic but was otherwise polite and had good insight into his current cognitive problems. Cranial nerve, motor, sensory, and cerebellar examination results were normal. Initial cell blood count, basic metabolic panel, liver function panel, thyroid studies, markers of inflammation, vitamin B12, folate, and urine analysis were all normal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".