A national portfolio of bone oncology trials—The Canadian experience in 2012
Bibliographic record
Abstract
BACKGROUND: The impact of both cancer and its treatment on bone is an essential component of oncological practice. Bone oncology not only affects patients with both early stage and metastatic disease but also covers the entire spectrum of tumour types. We therefore decided to review and summarise bone oncology-related trials that are currently being conducted in Canada. METHOD: We assessed ongoing and recently completed trials in Canada. We used available North American and Canadian cancer trial websites and also contacted known investigators in this field for their input. RESULTS: Twenty seven clinical trials were identified. Seven pertained to local treatment of bone metastasis from any solid tumour type. Seven were systemic treatment trials, five focused on bone biology and predictive factors, three evaluated safety of bone-targeted agents, three were adjuvant trials and two trials investigated impact of cancer therapy on bone health. The majority of trials were related to systemic treatment and bone biology in breast cancer. Most were small, single centre, grant-funded studies. Not surprisingly the larger safety and adjuvant studies were pharmaceutical company driven. DISCUSSION: Despite the widespread interest in bone-targeted therapies our survey would suggest that most studies are single centre and breast cancer focused. If major advances in bone oncology are to be made then collaborative strategies are needed to not only increase current sample sizes but to also expand these studies into non-breast cancer populations.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".