Mobile solution for clinical research documentation.
Bibliographic record
Abstract
153 Background: As trial complexity increases, there is a growing need to facilitate rapid communication of trial data among members of the research and clinical care teams. Timeliness of investigator review and high quality data are critical to clinical trial documentation. Princess Margaret developed an electronic application that meets these needs, is integrated into a patient’s medical chart, and is accessible at the point of care on a mobile device. Methods: Focus groups were held with research staff to evaluate application and device needs. Device needs were usability, compatibility with the electronic patient record (EPR), and encryption ability, which were considered and a device was selected. Standard templates were developed for the informed consent process, clinical notes, vital signs, baseline symptoms, adverse events, and concomitant medications. Electronic source (eSource) was linked to an existing CTMS, the Clinical Research Record. eSource is accessible via the EPR, allows for electronic review and sign off by investigators, and allows data capture directly into the EPR from a mobile device. Results: A 4 month pilot was completed in December 2013 with 10 clinical research nurse coordinators (CRNCs) from 3 different treatment areas, enrolling 40 patients. The project continues to roll out and will be completely integrated by July 2014. CRNCs enter information at the point of care that is easily shared amongst research and clinical care teams; important safety information is available to all staff; investigators can sign off electronically; and data has an audit trail. Metrics collected over a two-month span demonstrate average investigator sign off is completed within 7 days, well under our established timelines of within a cycle of treatment. Conclusions: Enhancements were made within the system during the pilot to facilitate workflow as issues were identified. The system provides solutions for delayed documentation, the transfer of paper charts between team members, and investigator sign-off. This has allowed for tracking of metrics including the timeliness of documentation, review, and sign off; and quality improvements to documentation will be measured through internal quality assurance reviews.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.150 |
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".