Bibliographic record
Abstract
Background: The indication of plasmapheresis in thyroid storm is currently a category III in the American Society for Apheresis and a recommendation grade IIc. The impact of plasmapheresis on mortality in unstable patients due to thyrotoxicosis is not well studied. Case: A 56-year woman with untreated Graves disease was transferred from a psychiatric institution for atrial fibrillation and acute dyspnea. Her vital signs were a blood pressure of 115/60, a pulse of 190 bpm and a body temperature of 41.3°C. On presentation, she was confused; she had bilateral exophthalmia and slight pedal edema. Her TSH was <0.004 mU/L and a T4 at 37 µg/dl and her T3 at 1.7 µg/dl. In the past, she had a 99mTc scintigraphy that showed diffuse uptake and her TSI was positive, but she refused treatment. An X-Ray showed pneumonia. Upon initial evaluation, her Burch and Wartofsky score was 85, so she was immediately started on propylthiouracil, solucortef, lugol, esmolol which was later changed to propranolol. In the following hours, she developed hypotension and heart failure with pulmonary edema and a rise in hepatic enzymes. Due to her hemodynamic instability and the high mortality rate with thyroid storm, it was decided to proceed to a plasmapheresis. She had two plasmaphereses in the following two days and her T4 lowered from 37 µg/dl to 23 µg/dl after the first plasmapheresis and from 23 µg/dl to 14 µg/dl after the second plasmapheresis. Considering her tachyarrhythmia was difficult to control despite beta-blockers and digoxin, she had an electric cardioversion between the two plasmaphereses and has stayed in sinus rhythm since. After her second plasmapheresis, her level of consciousness and her Burch Wartofsky score improved. She was controlled by medication afterwards and left with methimazole and small dose of propanolol. Conclusion: We believe that plasmapheresis helped manage the patient’s thyroid storm and her hemodynamic instability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".