DEVELOPING AN IPAD<sup>®</sup> APPLICATION FOR DATA COLLECTION IN A RHEUMATOLOGY RESEARCH CLINIC
Bibliographic record
Abstract
OBJECTIVES: Clinical research data are often collected on paper and later inputted onto an electronic database. This method is time consuming and potentially introduces errors. Therefore, to make primary data collection more efficient and less error prone we aimed to develop a touch-screen application for data collection in a psoriatic arthritis research clinic and compared it with the pre-existing paper-based system. METHODS: We developed a Web application using Java and optimized it for the iPad®. It highlights missing fields for physicians in real time, and only permits submission of data collection form after corrections are made. For its evaluation, seven physicians participated, and before each patient visit they were randomly assigned paper or iPad® data entry. Number of errors, length of visit, and time between clinic visit and completion of data entry were measured. RESULTS: A total of 106 patients seen in the clinic who agreed to participate were randomly assigned to be evaluated by clinic physicians using the iPad® (fifty-three patients) or a paper protocol (fifty-three patients). On average, 3.34 omissions were found per paper form, of which 2.24 would have been detected on the iPad®. The iPad® increased the mean patient encounter time from 37.2 minutes to 46.5 minutes, but eliminated delay between a clinic visit and its data entry. CONCLUSIONS: Entering data using the iPad® application makes the patient encounter slightly longer, but reduces "missing fields." It also eliminates the delay between clinic visit and data entry thus improving the efficiency of clinical data capture in a research setting.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".