Current Status and Future Expectations of Using Remote Source Data Verification for Improving the Efficiency of Clinical Trials
Bibliographic record
Abstract
Background: We conducted a questionnaire survey on clinical research associates (CRAs) of pharmaceutical companies and contract research organizations (CROs) to investigate the current status of using remote source data verification (SDV) in Japan, and to evaluate the problems related to remote SDV.Methods: The survey was performed using a self-administered questionnaire posed on the web site for CRAs in Japan. The questionnaire survey was carried out from 11 to 25 October 2011.Results: There were 640 responses from CRAs. Sixty-four percent of all respondents knew about remote SDV, and 33 CRAs had experienced using remote SDV. Regarding the possibility of using remote SDV in on-the-job training (OJT), 82% of inexperienced CRAs and 76% of experienced CRA responded “possible”. In terms of the expectation of remote SDV, 91% of experienced CRAs and 86% of inexperienced CRAs responded that they would like to use remote SDV. Although the expectation is high, 76% of CRAs responded that the standard operating procedure (SOP) describing the use of remote SDV was not available in their companies or CROs. The interval of site visit had extended significantly in sites implementing remote SDV compared with visits made by inexperienced CRAs in sites implementing standard SDV (p<0.001).Conclusions: The present survey showed that although remote SDV has not yet been widely implemented, there is high expectation of using remote SDV among the CRAs.The result also suggested that using remote SDV might decrease the time spent onsite and the frequency of onsite visits. The survey also showed that SOP for remote SDV is not available in many pharmaceutical companies and CROs. Guidelines and SOPs have to be established in the near future. (Jpn J Clin Pharmacol Ther 2013; 44(1): 47-52)
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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 | 0.172 | 0.268 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".