Provocative questions in osteosarcoma basic and translational biology: A report from the Children's Oncology Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Patients who are diagnosed with osteosarcoma (OS) today receive the same therapy that patients have received over the last 4 decades. Extensive efforts to identify more effective or less toxic regimens have proved disappointing. As we enter a postgenomic era in which we now recognize OS not as a cancer of mutations but as one defined by p53 loss, chromosomal complexity, copy number alteration, and profound heterogeneity, emerging threads of discovery leave many hopeful that an improving understanding of biology will drive discoveries that improve clinical care. Under the organization of the Bone Tumor Biology Committee of the Children's Oncology Group, a team of clinicians and scientists sought to define the state of the science and to identify questions that, if answered, have the greatest potential to drive fundamental clinical advances. Having discussed these questions in a series of meetings, each led by invited experts, we distilled these conversations into a series of seven Provocative Questions. These include questions about the molecular events that trigger oncogenesis, the genomic and epigenomic drivers of disease, the biology of lung metastasis, research models that best predict clinical outcomes, and processes for translating findings into clinical trials. Here, we briefly present each Provocative Question, review the current scientific evidence, note the immediate opportunities, and speculate on the impact that answered questions might have on the field. We do so with an intent to provide a framework around which investigators can build programs and collaborations to tackle the hardest problems and to establish research priorities for those developing policies and providing funding.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it