Identifying emergency-sensitive conditions for the calculation of an in-hospital standardized mortality ratio specific to emergency care
Bibliographic record
Abstract
by mistake.Her only concern now was to go home.I assured her we could help her with this request.Once we found she was tolerating her oral fluids, obtained a urine spec, and started her on antibiotics for a UTI, we were able to arrange her discharge, with a referral to Home Health to ensure she had appropriate support to manage well.Caring for seniors with dementia is not often this simple, but the reward is in finding which techniques help us to make contact with the person, and communicate that we are doing our best to help. About the authorCathy Sendecki has worked in Burnaby Hospital ED since 1987.As Educator, in 2005 she worked with the Clinical Nurse Specialist for Acute Care of Older Adults to improve the care of seniors in our ED.What started as a three-month project by an ED nurse who did not see great areas for improvement, became a full-time position that continues to be fascinating and challenging.She appreciates the opportunity to assess patients with a geriatric and emergency "lens" to assist the emergency team to provide the best care to those seniors with complex presentations.About.com(2013, February).Alzheimer's/Dementia. What not to do to people with Alzheimer's Disease-10 pet peeves.Retrieved from http://alzheimers.about.com/od/communication/a/What-Not-To-Do-To-People-With-Alzheimers-Disease-10-Pet-Peeves.htmAlzheimer Society of British Columbia.www.alzheimerbc.orgAlzheimer Society of Canada.www.alzheimer.caBaycrest (n.d.).Memory loss and dementia: Session 3:
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".