Characterization of international partnerships in global retinoblastoma care and research: A network analysis
Bibliographic record
Abstract
Global cooperation is an integral component of global health research and practice. One Retinoblastoma World (1RBW) is a cooperative network of global treatment centers that care for children affected by retinoblastoma. The study aimed to determine the number, scope and nature of collaborations within 1RBW, and uncover how they are perceived to contribute towards improving retinoblastoma outcomes. A cross-sectional, mixed-methods egocentric network analysis was conducted. Treatment centers (n = 170) were invited to complete an electronic survey to identify collaborative activities between their institution (ego), and respective partners (alters). Network maps were generated to visualize connectivity. Key informants (n = 18) participated in semi-structured interviews to add details about the reported collaborations. Interviews were analysed through inductive thematic analysis. Surveys were completed by 56/170 (33%) of 1RBW treatment centers. Collectively, they identified 112 unique alters (80 treatment centers; 32 other organizations) for a total network size of 168 nodes. Most collaborations involved patient referrals, consultations and twinning/capacity building. Interviews identified four main themes: conceptualization of partnership; primary motivation for collaborations; common challenges to collaboration; and benefits to partnership. There is extensive global collaboration to reduce global retinoblastoma mortality, but there is room to expand connectivity through active efforts to include actors located at network peripheries.
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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".