Incidence, Morbidity, and Mortality of Terson Syndrome in Hamilton, Ontario
Bibliographic record
Abstract
OBJECTIVE: Evaluate the incidence, neurologic morbidity, and mortality of patients with Terson syndrome. METHODS: Consecutive patients admitted to the Hamilton General Hospital from May 2012 to May 2013 with a diagnosis of spontaneous subarachnoid hemorrhage (SAH) were recruited. Funduscopic examinations were performed under pharmacological mydriasis. Outcome measures included: (1) the presence or absence of Terson syndrome; (2) The Glasgow Coma Scale (GCS), Hunt and Hess scale (H&H), and SAH Fisher score upon admission to the hospital; (3) the modified Rankin score upon discharge; and (4) and all-cause mortality. RESULTS: Forty-six patients were included and 10 had Terson syndrome (21%). The median H&H, GCS, and Fisher scores were 4, 6.5, and 4.0 for patients with Terson syndrome vs. 2, 14, and 3 for patients without Terson syndrome (p=0.0032, 0.0052, and 0.031), respectively. The median Rankin score was 6 for patients with Terson syndrome vs. 3.5 for patients without Terson syndrome (p=0.0019). The odds of all-cause mortality with Terson syndrome vs. no Terson syndrome was 12: 1 (95% confidence interval 2.33-61.7), p =0.003. Only four of the 10 patients with Terson syndrome survived. CONCLUSIONS: Based on this study, approximately one-fifth of patients admitted to the hospital with a spontaneous SAH could have Terson syndrome. Patients with Terson syndrome have significantly worse GCS and H&H scores upon admission to the hospital, lower modified Rankin scores upon discharge, and greater mortality. Thus, Terson syndrome is not rare among patients with SAH and carries a worse prognosis.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".