Severe Symptomatic Hyponatremia Due to Syndrome of Inappropriate Antidiuretic Hormone From Mild Closed Head Trauma and Concussion
Bibliographic record
Abstract
We report the first published case of severe, symptomatic hyponatremia resulting from syndrome of inappropriate antidiuretic hormone (SIADH) incurred as the result of a blow to the head inducing a concussion without any additional signs of head injury. A 38-year-old male in excellent health suffered a fall from a tree causing a right leg fracture and blunt trauma to the head with concussion but no other signs of head injury. The patient was discharged several days after surgery for repair of his leg fracture and subsequently became severely ill with nausea, vomiting, headache, and seizures. He was readmitted with a serum sodium of 114 mEq/L (mmol/L) with a presumed diagnosis of dehydration. After several days of saline administration he was discharged, but within 24 hours he again experienced severe, symptomatic hyponatremia and was admitted for the third time with a serum sodium of 110 mEq/L (mmol/L). A diagnosis of SIADH was made, and the patient was successfully treated with fluid restriction, intravenous saline, and demeclocycline resulting in a full recovery without further incident. This case emphasizes the need to consider SIADH as a possible cause of severe hyponatremia in patients sustaining relatively minor closed head trauma. J Med Cases. 2015;6(1):27-29 doi: http://dx.doi.org/10.14740/jmc2001w
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".