International Referral and Elderly Care—A Case of Atypical Parkinsonism and Cerebellar Atrophy
Bibliographic record
Abstract
A 66-year-old male patient had a history of hepatitis B, was a hepatitis C carrier, and had hypertension. He was referred to our family medicine international clinic by the Canadian Physician's Referral Service. He had had progressive general weakness over 2 years, and the symptom had recently exacerbated. He also had slurred speech, difficulty in swallowing with occasional choking, constipation, and urinary incontinence. He was diagnosed as having Parkinson disease, but his symptoms worsened despite treatment with levodopa 250mg four times daily for 2 years. He had sought medical help in many clinics in Taiwan and Canada and was referred to our outpatient clinic by his Canadian family physician. The neurologist suggested that the diagnosis was multiple system atrophy (MSA) after history taking and neurologic examination. Rehabilitation programs, including physical therapy, occupational therapy and speech therapy, were arranged for him. Many Taiwanese immigrants prefer to come back to Taiwan for medical treatment. If we can integrate medical resources not only between different hospitals but also between different countries, many unnecessary medical expenses could be avoided. In this case, we summarized the case history and provided him with a CD of the images. This will be helpful in further care by a Canadian physician.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".