Cancer Care Closer to Home: Erie Shores HealthCare experience
Bibliographic record
Abstract
The increasing prevalence of cancer diagnoses highlights a growing need for resources that provide ongoing therapies closer to patients' homes, along with improved support for caregivers. Transportation challenges related to travelling from a patient's residence to treatment facilities can significantly hinder access to timely diagnoses and quality cancer care. Recognizing this pressing healthcare need, Erie Shores HealthCare (ESHC), a community hospital in the region, has partnered with the Erie St. Clair Regional Cancer Program (ESCRCP), a specialized program for cancer care, to establish a satellite clinic in Leamington, Ontario—a rural, agriculture-based town in Southern Canada. Historically, residents of Leamington and its surrounding communities have had to travel to Windsor or Chatham, approximately 50 km away, to receive chemotherapy treatments. For patients in advanced stages of cancer or receiving palliative care for their cancer diagnosis, long-distance travel or travelling during inclement weather poses a considerable challenge. Patients have expressed a strong desire for care to be delivered closer to home, which could potentially reduce their travel time and alleviate travel-related stress, thereby mitigating the psychosocial burdens associated with cancer. This publication details the development and operation of a fully integrated decentralized cancer care program at a satellite clinic located within a local hospital in this rural region of Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".