Electronic Versus Traditional Data Collection: A Multicenter Randomized Controlled Perioperative Pain Trial
Bibliographic record
Abstract
Background: Electronic data collection is increasingly available as a means to collect pain-related clinical trial data; however, effectiveness and costs relative to traditional data collection are uncertain.Aims: The aim of this study was to evaluate data quality, protocol adherence, satisfaction, and resource requirements of electronic data collection (i.e., Internet-based electronic submission) compared to traditional data collection methods (i.e., paper-based diaries and telephone interviews) in a perioperative factorial randomized controlled trial.Methods: This study was an open-label two-arm parallel randomized controlled trial. Women (18–75 years) undergoing breast cancer surgery were allocated to either electronic or traditional data collection and completed pain-related questionnaires at baseline, postoperative period, and 3-month follow-up (NCT02240199).Results: We acquired outcome data at all time points from 78 randomized patients, 38 in the electronic group and 40 in the traditional group. The number of data queries (e.g., erroneously entered data) per patient was higher in the electronic data group (4.92 [SD = 4.67] vs. 1.88 [SD = 1.51]; P < 0.001). No between-group differences were observed for compliance with medications, data completeness, loss to follow-up, or patient or research assistant satisfaction. More research assistant time per patient was spent collecting data in the traditional group (42.6 min [SD = 12.8] vs. 9.92 min [SD = 7.6]; P < 0.001); however, costs per patient were higher in the electronic group ($176.85 [SD = 2.90] vs. $16.33 [SD = 4.90]; P < 0.001).Conclusion: Electronic data collection is feasible for perioperative pain clinical trials. Additional trials, including different surgical populations, are needed to confirm our findings and optimize use of electronic data capture methods.
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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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".