Research Opportunities and Collaborative Multisite Studies in Psychosocial Hematology/Oncology
Bibliographic record
Abstract
Over the past several decades, dramatic improvements in outcome have occurred for children treated for cancer. Many of these advances can be attributed to the benefits of multicenter research conducted within the context of a cooperative group clinical trials infrastructure (D’Angio & Vietti, 2001; Pediatric Oncology Group, 1992). Historically, the cooperative groups sponsored by the National Cancer Institute provided pooled expertise, centralized high-quality medical informatics resources, and access to large patient populations. This infrastructure enabled investigators to ask more focused research questions with greater statistical power as well as generalize research findings to the broader population. Although childhood cancer is by no means a rare disease, its incidence in the general population is sufficiently low that few single pediatric oncology treatment centers are likely to treat enough patients, representing an adequately homogeneous sample, to provide a robust evaluation of clinical outcomes. In many respects, multisite research has been necessary to acquire adequate sample sizes to allow appropriate statistical evaluations of treatment outcomes and generalization of these outcomes to the larger pediatric oncology population. Awareness of this fact led first to the development of small consortia of pediatric oncology centers and later to the formation of large multiinstitutional cooperative study groups to conduct controlled clinical therapeutic trials for pediatric cancer patients. Ultimately, the four major childhood cancer study groups (the Children’s Cancer Group, CCG; the Pediatric Oncology Group, POG; the National Wilms Tumor Study Group; and the Intergroup Rhabdomyosarcoma Study Group) merged in 2000 to form a single collaborative group: the Children’s Oncology Group (COG). At present, the 238 institutions that comprise the COG provide the research infrastructure for the majority of pediatric oncology clinical trials conducted in North America, Australia, and parts of Europe. Moreover, because the COG member institutions include all major university and teaching hospitals throughout the United States and Canada, the majority of children diagnosed with cancer in North America will be treated at a COG member institution with the opportunity to be enrolled on a COG protocol. An early evaluation of referral patterns to the two largest cooperative groups enumerated the observed cancer cases from the CCG and POG cancer incidence registries.
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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 | 0.524 | 0.368 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".