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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".