Clinical Reasoning: An 81-year-old woman with confusion, weakness, and left-sided hemineglect
Bibliographic record
Abstract
An 81-year-old right-handed woman presented to the emergency department (ED) with confusion, generalized weakness, and difficulty ambulating. In addition to several cardiovascular comorbidities (hypertension, hyperlipidemia, peripheral arterial disease, coronary artery disease, chronic kidney disease, and renal artery stenosis), her medical history was significant for stage IV carcinoma of the urinary bladder. She had been in her usual state of health until 1 week prior to her presentation when she began experiencing generalized weakness in her legs, requiring more frequent use of a walker as well as intermittent headaches and increasing forgetfulness. Two to 3 days prior to presentation, her husband noted that she was increasingly getting confused. On the day of presentation, she woke up disoriented and was too weak to get out of bed, prompting her husband to bring her to the ED. In the ED, she was afebrile, hypertensive (180/90 mm Hg), and tachycardic (heart rate 120–130/min). On mental status examination, she was intermittently oriented to time and place and unable to follow complex instructions. Her score on the Montreal Cognitive Assessment (MoCA) was 9/30. Other pertinent examination findings included left facial weakness (lower face) and left upper and lower extremity weakness (3/5). Sensory examination revealed left-sided hemineglect. The patient was unable to ambulate as she could not stand without assistance.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".