The Development of a Standardized Software Platform to Support Provincial Population-Based Cancer Outcomes Units for Multiple Tumour Sites: OaSIS - Outcomes and Surveillance Integration System
Bibliographic record
Abstract
Understanding the impact of treatment policies on patient outcomes is essential in improving all aspects of patient care. The BC Cancer Agency is a provincial program that provides cancer care on a population basis for 4.5 million residents. The Lung and Head & Neck Tumour Groups planned to create a generic yet comprehensive software infrastructure that could be used by all Tumour Groups: the Outcomes and Surveillance Integration System (OaSIS). The primary goal was the development of an integrated database that will amalgamate existing provincial data warehouses of varying datasets and provide the infrastructure to support additional routes of data entry, including clinicians from multiple-disciplines, quality of life and survivorship data from patients, and three dimensional dosimetric information archived from the radiotherapy planning and delivery systems. The primary goal is to be able to capture any data point related to patient characteristics, disease factors, treatment details and survivorship, from the point of diagnosis onwards. Through existing and novel data-mining techniques, OaSIS will support unique population based research activities by promoting collaborative interactions between the research centre, clinical activities at the cancer treatment centres and other institutions. This will also facilitate initiatives to improve patient outcomes, decision support in achieving operational efficiencies and an environment that supports knowledge generation.
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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".