Centrality in Collaboration: Community Detection for Oncology Researchers
Bibliographic record
Abstract
Background: Cancer patients who do not respond to standard treatments often become candidates for clinical trials. It is known that the collaboration networks between oncologists is a primary factor for further engagement in subsequent trial enrollment. Social network analysis (SNA) and community detection algorithms can be used to explore collaboration patterns using data from the Princess Margaret Clinical Research Record and Cancer Registry. Objectives: The data consists of 2970 patients in 515 clinical trials between January 2016 to December 2018. We are interested in the referral patterns among patients enrolled in multiple trials. Among 389 patients enrolled in more than one of 288 trials, we aim to identify collaboration networks based on intervention type. Methods: We applied community detection algorithms, including Girvan-Newman and Louvain, and compared them to an author-developed algorithm that utilizes degree centrality and directionality in participant enrollments to identify influential interventions. Community detection was assessed by superimposing sociograms and visualizing the contrast between the in-degree/out-degree distribution of participant enrollments. Results: Application of Girvan Newman, which incorporates edge betweenness but not degree centrality, and Louvain, which does not consider directionality, did not yield informative structure for oncology collaboration networks. The algorithm developed by the authors found intuitively descriptive communities that were supported by the sociograms and visualized in-degree/out-degree distribution. Conclusions: SNA approaches utilizing a combination of degree centrality and edge betweenness can interpret oncology collaboration networks. With this perspective, it is possible to delve deeper into patient engagement and improve the organizational design of clinical trial referrals.
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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 | 0.010 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".