Prognostication of long‐term outcomes after subarachnoid hemorrhage: The FRESH score
Bibliographic record
Abstract
OBJECTIVE: To create a multidimensional tool to prognosticate long-term functional, cognitive, and quality of life outcomes after spontaneous subarachnoid hemorrhage (SAH) using data up to 48 hours after admission. METHODS: Data were prospectively collected for 1,619 consecutive patients enrolled in the SAH outcome project July 1996 to March 2014. Linear models (LMs) were applied to identify factors associated with outcome in 1,526 patients with complete data. Twelve-month functional, cognitive, and quality of life outcomes were measured using the modified Rankin scale (mRS), Telephone Interview for Cognitive Status, and Sickness Impact Profile. Based on the LM residuals, we constructed the FRESH score (Functional Recovery Expected after Subarachnoid Hemorrhage). Score performance, discrimination, and internal validity were tested using the area under the receiver operating characteristic curve (AUC), Nagelkerke and Cox/Snell R(2) , and bootstrapping. For external validation, we used a control population of SAH patients from the CONSCIOUS-1 study (n = 413). RESULTS: The FRESH score was composed of Hunt & Hess and APACHE-II physiologic scores on admission, age, and aneurysmal rebleed within 48 hours. Separate scores to prognosticate 1-year cognition (FRESH-cog) and quality of life (FRESH-quol) were developed controlling for education and premorbid disability. Poor functional outcome (mRS = 4-6) for score levels 1 through 9 respectively was present in 3, 6, 12, 38, 61, 83, 92, 98, and 100% at 1-year follow-up. Performance of FRESH (AUC = 0.90), FRESH-cog (AUC = 0.80), and FRESH-quol (AUC = 0.78) was high. External validation of our cohort using mRS as endpoint showed satisfactory results (AUC = 0.77). To allow for convenient score calculation, we built a smartphone app available for free download. INTERPRETATION: FRESH is the first clinical tool to prognosticate long-term outcome after spontaneous SAH in a multidimensional manner. Ann Neurol 2016;80:46-58.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".