Application of PHASES and ELAPSS scores to ruptured cerebral aneurysms: how many would have been conservatively managed?
Bibliographic record
Abstract
BACKGROUND: We calculated the PHASES and ELAPSS scores for a large cohort of ruptured intracranial aneurysms (RIA) in order to determine whether these RIA would have been pre-emptively treated or closely followed-up should they have been detected prior to rupture. METHODS: We retrospectively reviewed a consecutive series of RIA over a 20-year period. The primary outcome of this study was the PHASES score of each ruptured aneurysm included. Secondary outcomes were ELAPSS score and other risk factors for aneurysmal subarachnoid hemorrhage including aneurysm location, aneurysm size, aneurysm morphology, smoking and hypertension history, personal and family history of subarachnoid hemorrhage. Multiplicity of cerebral aneurysms was recorded. Descriptive statistics are reported. RESULTS: 700 consecutive ruptured aneurysms were included. Mean age at rupture was 56 (+/-13.5) years. Mean aneurysm size was 5.9 (+/-2.5) mm. Most common locations of ruptured aneurysms were the anterior cerebral/communicating artery (39%), posterior communicating artery (21%), middle cerebral artery (16%) and basilar terminus (7%). Mean PHASES score was 5.3 (+/-2.5) and 17% of the RIA had a PHASES score of 3 or less. Mean ELAPSS score was 13.89 (+/-7.05) and over half of the RIA included had a low risk of future growth. CONCLUSIONS: A reasonable percentage of ruptured aneurysms have a low calculated PHASES score and these aneurysms may have been managed conservatively should they have presented incidentally prior to rupture. Most ruptured aneurysms also had a low ELAPSS score and were at low risk of future growth. The use PHASES score and ELAPSS score alone when making treatment decisions could result in many aneurysms being treated conservatively or undergoing remote surveillance despite rupture potential.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".