Abstract TP425: How do Different Outcome Measures Reflect Outcome After Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
Few treatments for aneurysmal subarachnoid hemorrhage (aSAH) have been effective in randomized clinical studies. One reason may be that the outcome measures used are not sensitive enough to detect efficacy of treatments in this disease. This hypothesis was examined by comparing 6 outcome measures for 72 patients with aSAH. Patients with aSAH who were World Federation of Neurological Surgeons grades 2 to 4 with an external ventricular drain inserted as part of standard of care were entered in a Phase 1/2a multicenter, controlled, randomized, open-label, dose escalation study to determine the maximum tolerated dose and safety and tolerability of a sustained release formulation of nimodipine (EG-1962, NEWTON study) in patients with aSAH. Clinical outcome was assessed at 90 days after aSAH using the extended Glasgow outcome scale (eGOS), modified Rankin scale (mRS), Montreal cognitive assessment (MoCA), telephone interview of cognitive status (TICS), NIHSS and Barthel index. The relationship between each outcome measure and the eGOS was plotted on arithmetic graphs (Figure). The eGOS and mRS gave very similar results. More detailed cognitive assessments (MoCA, TICS) were more exponential in shape with more variability. The NIHSS and Barthel had outcomes clustered towards the highest ends of the scales with distributions that did not discriminate as much as the eGOS or mRS. The MoCA and TICS gave similar results. It was concluded that the eGOS or mRS produce a similar and varying range of outcomes after aSAH, whereas cognitive assessments like the MoCA and TICS and scales designed for ischemic stroke like the NIHSS and BI are less discriminatory of outcomes after aSAH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".