Rhabdomyolysis causing acute kidney injury in a patient with multiple risk factors and an underlying inflammatory muscle disease: A Case Report
Bibliographic record
Abstract
A 33 year-old Caucasian male with underlying type one diabetes mellitus, dyslipidemia, hypothyroidism and nephrotic syndrome secondary to membranous glomerulonephropathy (GN), presented with new onset tonic-clonic seizures (lasting one minute) after an episode of binge drinking in which there was a considerable period of immobilization. The patient was taking 80 mg of Atorvastatin daily at the time of presentation. The patient was intubated and ventilated and initial laboratory investigations confirmed a creatine kinase (CK) of 12,000 U/L (Normal 20-220 U/L) and creatinine of 3.1 mg/dL (Normal 0.7 – 1.3 mg/dL) and associated hyperkalemia of 5.6-5.9 mmol/L (Normal 2.5-5.0 mmol/L) The patient developed acute kidney injury (AKI) presumed secondary to rhabdomyolysis, and required renal replacement therapy. His CK eventually peaked at 153,741 U/L on the seventh day of admission. The patient regained consciousness and improved clinically over the following weeks. A muscle biopsy performed on the 29th day of admission showed changes consistent with polymyositis. On follow up testing the patient’s TSH was noted to be 101.99 mIU/L (Normal 0.5-5.0 mIU/L ) with a T4 of 5.5 pmol/L (Normal 8.5-15.2 pmol/L) indicating uncontrolled hypothyroidism. This case highlights multiple potential etiologies causing rhabdomyolysis that may occur concurrently in a patient and contribute to AKI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".