Measuring the Impact of Delayed Cerebral Ischemia on Neuropsychological Outcome After Aneurysmal Subarachnoid Hemorrhage—Protocol of a Swiss Nationwide Observational Study (MoCA–DCI Study)
Bibliographic record
Abstract
BACKGROUND: The exact relationship between delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH) and neuropsychological impairment remains unknown, as previous studies lacked a baseline examination after aneurysm occlusion but before the DCI-period. Neuropsychological evaluation of acutely ill patients is often applied in a busy intensive care unit (ICU), where distraction represents a bias to the obtained results. OBJECTIVE: To evaluate the relationship between DCI and neuropsychological outcome after aSAH by comparing the Montreal Cognitive Assessment (MoCA) results in aSAH patients with and without DCI at 3 mo with a baseline examination before the DCI-period (part 1). To determine the reliability of the MoCA, when applied in an ICU setting (part 2). METHODS: Prospective, multicenter, and observational study performed at all Swiss neurovascular centers. For part 1, n = 240 consecutive aSAH patients and for part 2, n = 50 patients with acute brain injury are recruited. EXPECTED OUTCOMES: Part 1: Effect size of the relationship between DCI and neuropsychological outcome (MoCA). Part 2: Reliability measures for the MoCA. DISCUSSION: The institutional review boards approved this study on July 4, 2017 under case number BASEC 2017-00103. After completion, the results will be offered to an international scientific journal for peer-reviewed publication. This study determines the exact impact of DCI on the neuropsychological outcome after aSAH, unbiased by confounding factors such as early brain injury or patient-specific characteristics. The study provides unique insights in the neuropsychological state of patients in the early period 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".