Society for Immunotherapy of Cancer clinical and biomarkers data sharing resource document: Volume I—conceptual challenges
Bibliographic record
Abstract
The sharing of clinical trial data and biomarker data sets among the scientific community, whether the data originates from pharmaceutical companies or academic institutions, is of critical importance to enable the development of new and improved cancer immunotherapy modalities. Through data sharing, a better understanding of current therapies in terms of their efficacy, safety and biomarker data profiles can be achieved. However, the sharing of these data sets involves a number of stakeholder groups including patients, researchers, private industry, scientific journals and professional societies. Each of these stakeholder groups has differing interests in the use and sharing of clinical trial and biomarker data, and the conflicts caused by these differing interests represent significant obstacles to effective, widespread sharing of data. Thus, the Society for Immunotherapy of Cancer (SITC) Biomarkers Committee convened to identify the current barriers to biomarker data sharing in immuno-oncology (IO) and to help in establishing professional standards for the responsible sharing of clinical trial data. The conclusions of the committee are described in two position papers: Volume I-conceptual challenges and Volume II-practical challenges, the first of which is presented in this manuscript. Additionally, the committee suggests actions by key stakeholders in the field (including organizations and professional societies) as the best path forward, encouraging the cultural shift needed to ensure responsible data sharing in the IO research setting.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".