Accuracy of Simulated Research Tasks by Community Hospitals Participating in a Multicenter Telemedicine Trial
Bibliographic record
Abstract
Background/Aims: Clinical trials evaluating facility-to-facility telemedicine may include sites that have limited research experience. For the trial to be successful, these sites must correctly perform research-related tasks. This study aimed to determine whether health care professionals at community hospitals could accurately identify simulated study eligible patients and submit data to a research coordinating center. Methods: Twenty-seven community hospitals in the United States and Canada participated in this study. An electronic survey was sent to one designated health care professional at each site. The survey included a description of trial eligibility criteria and five written neonatal resuscitation scenarios. For each scenario, the participant determined whether the neonate was study eligible. One scenario required participants to submit 14 data elements to the coordinating center. Accuracy of study eligibility and data submission was summarized using standard descriptive statistics. Results: The survey response rate was 100% (27/27). Overall accuracy in determining study eligibility was 89% (120/135), and accuracy varied across the five scenarios (range 82–93%). Overall accuracy of data submission was 92% (310/336). Data were >95% accurate for 9 of the 14 data elements, with 100% accuracy achieved for 6 data elements. These results were used to clarify eligibility criteria, inform database design, and improve training materials for the subsequent clinical trial. Conclusions: Health care professionals at community hospitals accurately determined trial eligibility and submitted study data based on written clinical scenarios. Research teams conducting telemedicine trials with community hospitals should consider completing pre-trial simulation activities to identify opportunities for improving trial processes and materials.
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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 | 0.191 | 0.622 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".