A study of the workload and effectiveness of a comprehensive acute stroke service
Bibliographic record
Abstract
OBJECTIVE: To study the workload of and use of acute intervention within an established acute stroke service, the Calgary Stroke Programme (CSP). METHODS: Prospective record of all acute referrals, diagnoses, and management decisions over a 4 month period. RESULTS: The CSP received 572 referrals (median: 32 per week), 88% of which were made between 7 am and midnight. Of the 427 patients seen in person, 29% had not had an acute stroke or transient ischaemic attack (TIA). Fifty percent of patients with suspected acute stroke were referred within 3 h of symptom onset and 11% with acute ischaemic stroke (equating to 35% of those referred within 3 h of onset and seen in person) were treated with thrombolysis. CONCLUSION: Centralisation of services facilitates the rapid referral of, and use of acute interventions in, patients with acute stroke and TIA. Centralised services are likely to be busy (although less so at night), to attract large numbers of patients with disorders that mimic stroke and TIA, and yet still likely to treat only the minority of acute strokes using thrombolysis. These observations may help those planning similar services and underline the need to develop more widely applicable treatments for acute stroke.
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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".