Evaluating the impact of survivorship models on health system resources and costs.
Bibliographic record
Abstract
1 Background: The provincial cancer agency in Ontario developed and implemented a model of care (MOC) for breast cancer (BC) survivors to transition from oncology-lead care to primary care in a publically funded health care environment (2010-2013). Transition options included direct to primary care and stepped transition. The objective of our study was to examine the health system resources used by the women in the MOC group and compare them to those used by women who did not transition. Methods: A propensity score matched, quasi-experimental approach was used to compare the healthcare resource utilization and costs between BC survivors in the MOC program (case) and those receiving usual care (control). All MOC cases were linked using unique identifiers and linked into the provincial health system databases. Cases and controls were matched 1:1 on year of diagnosis and location of care and were followed from an index date to the earliest of her death date, date of last contact in the database, one day before another cancer diagnosis or the end of study available databases. The primary study outcome was overall health system utilization and mean cost during the follow-up period. Results: There were 2324 women in the MOC program. Demographic information (age, region, stage) were well balanced between cases and controls. Transitioned cases had lower hospitalizations (20.1% vs. 24.4%, p<0.05), fewer cancer clinic visits (6.0% vs. 15.1%, p<0.05), fewer medical oncologist visits (0.39 vs. 1.29, p<0.05) and fewer diagnostics (CT, MRI, ultrasound, x-rays) over an average of 25 months of follow-up. There was a trend for fewer family practice (7.35 vs. 7.91, p=0.08) and internal medical and hematology visits (0.81 vs. 1.03, p=0.08). Annual emergency visits were similar between the two groups (0.76 vs. 0.82, p=0.2). There was a $4300 (2012 $CAN) difference in the mean annual cost between cases and controls. Conclusions: Survivors in the MOC transition program used fewer health system resources and had lower health system costs when compared to controls. These findings provide real world evidence to inform transition policies for cancer survivors.
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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 | 0.036 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".