Optimizing dosing in navigation interventions across the cancer continuum: A planned secondary data analysis from the Patient Care Connect implementation.
Bibliographic record
Abstract
174 Background: Patient navigation programs in cancer care have historically focused on assisting persons to overcome barriers to accessing care. Evidence is emerging to support the impact of navigation interventions across the cancer continuum. However, navigation programs have varied designs, resulting in a lack of clarity about the optimal approach to delivering services to patients, and a lack of evidence linking program design to outcomes. Methods: A planned retrospective analysis of Medicare administrative claims for a population of older beneficiaries diagnosed with cancer: The main exposure was the number of contacts in person or over the phone with PCCP navigators in the 6 month period starting from the quarter in which patients enrolled in the PCCP. Repeated measures generalized linear models with normal distribution were used to evaluate trends in total cost over time based on: number of contacts, quarters post-enrollment (TIME), and the interaction between number of contacts and TIME. Intra-correlation was controlled for repeated measures. Results: 4,337 patients were included in this analysis. 17.9% had one contact, 17.7% had two, 22.2% had 3-4, 24.2% had 5-10, and 18.0% had more than 10 contacts. African Americans had a greater number of participants with more than 10 navigator contacts, as stage 4 cancers, and initial or end-of-life phase of care. Patients who received more than 3 contacts had significantly higher levels of baseline cost. Models to evaluate total cost over time demonstrate an effect of navigator contact on cost that is associated with number of contacts. This trend is statistically significant at 3-4 contacts or more, and remains significant at 10 or more contacts. Conclusions: Increased navigator contact is associated with increased slope of decline in utilization and cost indicates that navigation programs should be adequately resourced to deliver care that enables navigators to have contact with patients a minimum of 3-4 contacts over a six month period.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".