RESEARCH Factors influencing survival after stroke in Western Australia
Bibliographic record
Abstract
Around 90 % of these acute stroke events are estimated to result in hospital admission, with rapidly fatal cases not admitted. 2 About a quarter of people suffering stroke die within a month, and 40% within the first year. 3 A major influence on survival is the stroke subtype, with patients suffering haemorrhagic stroke more likely to die within the first 30 days than those suffering ischaemic stroke. 4 Access to treatments may also influence outcome. Early acute care is essential for optimal outcome, 5 but may be compromised by delays in response, transportation to an appropriate medical facility and diagnostic procedures. This hospital-based study aimed to determine risk factors that influence survival of stroke patients admitted to hospital for first-ever stroke throughout WA. The study was facilitated by the WA Data Linkage System, which links records of individuals from a number of health and administrative data sets, including hospital separation and death records. 6 This system enabled us to assess prognostic factors with high statistical power and to include rural and remote areas in the study, so that we could investigate the influence of place of residence and Aboriginality on stroke survival.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".