Building a Quality Improvement Coalition: A Cancer Information Management Strategy for Ontario
Bibliographic record
Abstract
ingly important. The proportion of disease and deaths from cancer is dramatically increasing, and there is a growing awareness of the broad continuum of cancer care. Currently, Ontario spends about $1.5 billion annually on cancer care with increasing pressures to invest more. As with other services within the broader health system, resources for cancer services are scarce, demand for services is increasing, complexity of patient care is rising, and navigating the system is becoming more challenging. While it is positive news that patients are living longer with cancer due to new and complex therapies, this trend places an increasing burden on services for cancer patients. In July 2001, the Cancer Services Implementation Committee was appointed by the Ontario Minister of Health in response to public concerns about waiting lists for radiation therapy and the ability of the current system to meet the growing need for cancer services of all kinds. The Committee found that the cancer system was fragmented and needed better coordination at the local and regional levels. While patients receive high-quality care through each portion of their care, there are few links between each portion, often leaving the patient with the responsibility of creating his or her own plan of care. Recommendations included integrating cancer services of the province’s regional cancer centres and their host hospitals, developing a cancer information system that would become the backbone for the integrated cancer system, and establishing a quality council to monitor, assess and improve cancer services. Figure 1 depicts the fragmented nature of the system. After climbing up the waiting list for each type of service, the patient joins another waiting list for the next required service. Figure 2 outlines the distribution of service delivery between different provider organizations.
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 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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".