Setting the IMPACT (IMProve Access to Clinical Trial data) Observatory baseline
Bibliographic record
Abstract
INTRODUCTION: The aim of the IMPACT (IMProving Access to Clinical Trial data) Observatory is to assess the transformation of clinical trials (CT) related to the evolution of sharing of CT data. The objective of this study is to establish a baseline for monitoring CT data sharing by the Observatory. MATERIALS AND METHODS: In this scoping review we searched for publications that address sharing, dissemination, transparency or reuse of CT data published prior to December 31st 2000. Two authors screened titles and abstracts of 1204 records received by Medline searches and added 47 publications from direct discovery. Four researchers extracted, coded, and analyzed the predefined information from 102 selected papers. RESULTS: We found a growing recognition of the importance of data sharing prior to 2001. However, there were numerous obstacles including the ambiguity of the concept of data sharing, the absence of specific terminology and the lack of an "open" culture. By the end of 2000, data, metadata, and evidence based medicine were defined. Data sharing, registries, databases and re-analyses of individual patient data (IPD) emerged. The use of systematic reviews and IPD meta-analysis in decision making was promoted. Most arguments for broader data sharing came from oncology, paediatrics, rare diseases, AIDS, pregnancy, perinatal medicine, and media reporting related scandals. CONCLUSIONS: Our findings indicate that the year 2000 could be used as a baseline for monitoring the evolution of CT data sharing as basic prerequisites were set in place, including greater understanding that CT data sharing is essential for decision making and the advancements of the Internet.
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 | 0.475 | 0.591 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".