Bibliographic record
Abstract
Technology in healthcare is rapidly catching up to other industries and the rate of change does not appear to be slowing down.Although most of us still have a Fax number and many have paper charts, system wide adoption Electronic Medical Records, Hospital Information Systems, and other emerging technologies have led to many improvements in patient care.With these advancements come new challenges.I believe there is a need and a patient demand for improved regional integration and coordination of care delivery.Urologists in Canada are well positioned to take advantage of these technological changes to improve care for our patients and to improve our job satisfaction.The government of Ontario has recently proposed a major overhaul to the structure of healthcare delivery in the province by creating "Ontario Health Teams" which may serve up to 300,000 people each.This change has been touted as the most significant organizational change in 50 years and is consistent with national trends towards healthcare integration.1 One stated rationale for the change is to improve coordination of care, mostly primary and home care, but also specialist care.It doesn't appear that the specialist role in an OHT has been defined, and this could be an opportunity for urologists to be proactive in defining our own role.In Urology, while there has been a positive, evidence-based push for regionalization of specialized surgeries, I believe that regional coordination of the "routine" care that we provide (consultations, diagnostic procedures) could also have a significant benefit for the overall urological health of our patients.There are 716 Urologists in Canada in 2018, or roughly one urologist for every 50K population.Compared to 2241 General Surgeons, or three surgeons for every 50K. 2 There are several cities in Canada that don't have a population to support an "in-house" urologist.It is difficult for many patients to travel.Many of our patients are elderly and frail, or don't have the means, or the support to travel out of town for surgery.By using new technology, we now can provide better care for these patients.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".