Thrombolysis in the air. Air-ambulance paramedics flying to remote communities treat patients before hospitalization.
Bibliographic record
Abstract
PROBLEM ADDRESSED: First Nations* communities in the North have a high prevalence of coronary artery disease and type 2 diabetes and face an increasing incidence of myocardial infarction (MI). Many conditions delay timely administration of thrombolysis, including long times between when patients first experience symptoms and when they present to community nursing stations, delays in air transfers to treating hospitals, uncertainty about when planes are available, and poor flying conditions. OBJECTIVE OF PROGRAM: To develop a program for administration of thrombolysis on the way to hospital by air ambulance paramedics flying to remote communities to provide more rapid thrombolytic therapy to northern patients experiencing acute MIs. COMPONENTS OF PROGRAM: Critical care flight paramedics fly to northern communities from Sioux Lookout, Ont; assess patients; communicate with base hospital physicians; review an exclusion criteria checklist; and administer thrombolytics according to the Sioux Lookout District Health Centre/Base Hospital Policy and Procedure Manual. Patients are then flown to hospitals in Sioux Lookout; Winnipeg, Man; or Thunder Bay, Ont. CONCLUSION: This thrombolysis program is being pilot tested, and further evaluation and development is anticipated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.095 | 0.018 |
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".