Bibliographic record
Abstract
Intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH) account for about 10–20% of strokes. Compared with ischemic stroke, neurological impairment tends to be more severe and outcomes are generally worse. An improved understanding of the pathophysiology of these conditions has increased the therapeutic options. With ICH, efforts focus on reducing early hematoma expansion and attenuating perihematomal edema. With SAH, clinicians must seek to prevent aneurysm rebleeding and limit both early and delayed ischemic injury. Prevention and timely recognition and treatment of potential causes of secondary brain injury, such as hydrocephalus, nonconvulsive seizures, intracranial hypertension, fever, anemia, hypoxemia, hypotension and hypo- or hyperglycemia, is crucial in maximizing the chance of a favorable recovery. Systemic complications, such as neurogenic stunned myocardium and pulmonary edema, must be recognized and treated appropriately. For patients with stupor and coma, physicians should communicate regularly with surrogate decision makers. Assessment of patients' prognosis should be transparent, based on best available evidence, and neither unrealistically optimistic nor pessimistic.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".