The Interface of Primary and Oncology Specialty Care: From Diagnosis Through Primary Treatment
Bibliographic record
Abstract
In this article, we review the challenges and opportunities related to developing effective, collaborative relationships between primary care and oncology providers during the initial cancer treatment period. This point in the cancer care continuum is complex and often represents the first major transition in care between primary care providers and oncology specialists. Patients often receive care from multiple providers in a number of different settings and are faced with making treatment decisions in a short, concentrated period of time. Patients consistently report having significant informational and emotional needs that are often unmet during this period. Using the published literature, we have identified a number of challenges during this part of the treatment continuum that may limit providers' ability to deliver effective care, including provider care discontinuities, information exchange problems, and gaps in provider role clarity that may be especially problematic within the context of managing comorbid health conditions. The limited published literature specific to this step in the cancer care trajectory supports the importance of ongoing primary care-specialist collaboration during this phase in the care continuum for both medical and psychosocial care. How to best achieve effective collaboration between providers requires further research in information exchange and tools to support it, evaluation of shared care models specific to the cancer context, and studies of the potential role of multidisciplinary case conferencing that include the primary care provider.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".