Challenges to complete and useful data sharing
Bibliographic record
Abstract
Data sharing from clinical trials is one way of promoting fair and transparent conduct of clinical trials. It would maximise the use of data and permit the exploration of additional hypotheses. On the other hand, the quality of secondary analyses cannot always be ascertained, and it may be unfair to investigators who have expended resources to collect data to bear the additional burden of sharing. As the discussion on the best modalities of sharing data evolves, some of the practical issues that may arise need to be addressed. In this paper, we discuss issues which impede the use of data even when sharing should be possible: (1) multicentre studies requiring consent from all the investigators in each centre; (2) remote access platforms with software limitations and Internet requirements; (3) on-site data analysis when data cannot be moved; (4) governing bodies for data generated in one jurisdiction and analysed in another; (5) using programmatic data collected as part of routine care; (6) data collected in multiple languages; (7) poor data quality. We believe these issues apply to all primary data and cause undue difficulties in conducting analysis even when there is some willingness to share. They can be avoided by anticipating the possibility of sharing any clinical data and pre-emptively removing or addressing restrictions that limit complete sharing. These issues should be part of the data sharing discussion.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reproducibility · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | MetaresearchOpen science Domain: Reproducibility · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.539 | 0.742 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.019 | 0.037 |
| Open science | 0.015 | 0.027 |
| Research integrity | 0.035 | 0.054 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".