Endovascular Therapy in Northeastern Ontario : Plotting Our Way To a Successful Regional Program To Enable Equitable Access For Northerners
Bibliographic record
Abstract
Five landmark trials, released in 2015, supporting strong evidence for endovascular mechanical clot retrieval (EVT), led to the identification of EVT as a new standard of hyperacute stroke care in the July 2015 update of Canadian Stroke Best Practice Recommendations. The region of Northeastern Ontario covers a large geography and has a population of over 500 000. Health Sciences North (HSN) in Sudbury, Ontario is one of the provinceu2019s nine designated Regional Stroke Centres (RSCs) and one which delivers r-tPA through its stroke-on-call physician team. Three other designated stroke centres (DSCs) provide t-PA supported by the provincial Telestroke Program. None of these DSCs in the region currently has EVT capability, meaning Northeasterners have no access to the treatment u201clocallyu201d. And given the many barriers to transfers to Southern Ontario, stroke patients with large vessel occlusions have less chance of a positive outcome. One such barrier is the current, two hour out-of-hospital transport time restriction followed by Emergency Medical Services in Ontario under the acute stroke protocol. To close this access gap, HSN is embarking on an implementation plan to develop an EVT program to service the entire region. A 20-person stakeholder group is in place and a draft timeline is done. Goal is to go live fiscal year 2019-20. Steps and progress can be included in the e-poster.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".