Choreographed expansion of services results in decreased patient burden without compromise of outcomes: An assessment of the Ontario experience
Bibliographic record
Abstract
Abstract Background Neuro-oncology care in Ontario, Canada has been historically centralized, at times requiring significant travel on the part of patients. Toward observing the goal of patient-centered care and reducing patient burden, 2 additional regional cancer centres (RCC) capable of neuro-oncology care delivery were introduced in 2016. This study evaluates the impact of increased regionalization of neuro-oncology services, from 11 to 13 oncology centers, on healthcare utilization and travel burden for glioblastoma (GBM) patients in Ontario. Methods We present a cohort of GBM patients diagnosed between 2010 and 2019. Incidence of GBM and treatment modalities were identified using provincial health administrative databases. A geographic information system and spatial analysis were used to estimate travel time from patient residences to neuro-oncology RCCs. Results Among the 5242 GBM patients, 79% received radiation as part of treatment. Median travel time to the closest RCC was higher for patients who did not receive radiation as part of treatment than for patients who did (P = .03). After 2016, the volume of patients receiving radiation at their local RCC increased from 62% to 69% and the median travel time to treatment RCCs decreased (P = .0072). The 2 new RCCs treated 35% and 41% of patients within their respective catchment areas. Receipt of standard of care, surgery, and chemoradiation (CRT), increased by 11%. Conclusions Regionalization resulted in changes in the healthcare utilization patterns in Ontario consistent with decreased patient travel burden for patients with GBM. Focused regionalization did not come at the cost of decreased quality of care, as determined by the delivery of a standard of care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".