Future research in cancer survivorship
Bibliographic record
Abstract
Since the inception of the Journal of Cancer Survivorship (JCSU), the founding editor-in-chief, the associate editors, editorial board members, and peer reviewers have played important roles in influencing the knowledge base published in JCSU. The journal editors represent key disciplines, practice areas, and research expertise in cancer survivorship, which includes oncology and hematology, pediatric oncology, health psychology/behavior medicine, primary care, oncology nursing, exercise oncology, epidemiology, rehabilitation sciences, health services research, health policy, public health, and sociology. Over the years, the journal editors have worked to facilitate peer review and provide quality reviews to prospective authors. Earlier this year, Chan et al. [1] provided an overview of the evolution of JCSU since its inception in 2007 to 2020. Chan et al.’s [1] paper included a comprehensive description of the content, impact metrics, and top cited papers. In addition, they created a neural network using the key terms of published articles (Fig. 1) that depicted both minor and major themes published since the inception of JCSU.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".