Hashimoto’s Encephalopathy: A Diagnosis in Disguise, Case Report and Review of Literature
Bibliographic record
Abstract
While Hashimoto’s encephalopathy (HE) is quite rare (there may only be several dozen diagnosed patients in the USA), it is also likely that there are many more undiagnosed sufferers. Because it is little known and its symptoms are primarily neurological, it is easy to misdiagnose or overlook and the symptoms frequently lead to mistaken neurological diagnoses. We report a case of an elderly female presenting with aphasia, cognitive decline, dysphagia and left hemiparesis as manifestation of this disorder. She failed to respond to high dose steroids and intravenous immunoglobulins which can be seen in rare subsets of patients. HE is a neuropsychiatric disorder of exclusion. It is important to identify this disease as most could be treated with steroids and immunosuppressants and therefore the term steroid-responsive encephalopathy associated with autoimmune thyroiditis (SREAT). Diagnosis is made in the first instance by excluding other toxic, metabolic and infectious causes of encephalopathy with neuroimaging and CSF examination. Response to treatment is quite variable despite most patients being steroid responsive. There are small subsets of patients who show poor response to steroids and immunoglobulins including this one and may require prolonged treatment to see any improvement. J Med Cases. 2014;5(12):643-645 doi: http://dx.doi.org/10.14740/jmc1997w
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".