Status of Cancer Care at Network Sites of the Nation’s Academic Cancer Centers
Bibliographic record
Abstract
BACKGROUND: Cancer care coordination across major academic medical centers and their networks is evolving rapidly, but the spectrum of organizational efforts has not been described. We conducted a mixed-methods survey of leading cancer centers and their networks to document care coordination and identify opportunities to improve geographically dispersed care. METHODS: A mixed-methods survey was sent to 91 cancer centers in the United States and Canada. We analyzed the number and locations of network sites; access to electronic medical records (EMRs); clinical research support and participation at networks; use of patient navigators, care paths, and quality measures; and physician workforce. Responses were collected via Qualtrics software between September 2017 and December 2018. RESULTS: Of the 69 responding cancer centers, 74% were NCI-designated. Eighty-seven percent of respondents were part of a matrix health system, and 13% were freestanding. Fifty-six reported having network sites. Forty-three respondents use navigators for disease-specific populations, and 24 use them for all patients. Thirty-five respondents use ≥1 types of care path. Fifty-seven percent of networks had complete, integrated access to their main center's EMRs. Thirty-nine respondents said the main center provides funding for clinical research at networks, with 22 reporting the main center provides all funding. Thirty-five said the main center provided pharmacy support at the networks, with 15 indicating the main center provides 100% pharmacy support. Certification program participation varied extensively across networks. CONCLUSIONS: The data show academic cancer centers have extensive involvement in network cancer care, often extending into rural communities. Coordinating care through improved clinical trial access and greater use of patient navigation, care paths, coordinated EMRs, and quality measures is likely to improve patient outcomes. Although it is premature to draw firm conclusions, the survey results are appropriate for mapping next steps and data queries.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".