A Comparison of Paper with Electronic Patient-Completed Questionnaires in a Preoperative Clinic
Bibliographic record
Abstract
UNLABELLED: In this unblinded randomized control trial we compared electronic self-administered Pre-Admission Adult Anesthetic Questionnaires (PAAQ) using touchscreen technology with pen and paper. Patients were recruited in the Pre-assessment Clinic if they had completed a PAAQ in the surgeon's office. Patients were randomized to study PAAQ using paper, hand-held computer (PDA), touchscreen desktop computer (kiosk), or tablet. Patients also completed a preference and satisfaction survey. The main outcome measures were percent agreement between the prestudy and study PAAQ and time to completion. Only six of the 366 patients approached refused to participate. The median time to completion of the PAAQ was shortest on the kiosk (2.3 min) and longest on the PDA (3.2 min) (chi2 = 14.5; P = 0.002). The mean agreement between the prestudy and the study PAAQ was approximately 94% across all study arms. The proportion of participants expressing comfort before and after completing the PAAQ increased from 10% to 97% on the computerized arms and from 60% to 64% on the paper arm. Touchscreen computer technology is an accurate, efficient platform for patient-administered PAAQ. Patients expressed comfort using the technology and preference for computerized versus paper for future questionnaires. IMPLICATIONS: Self-administered electronic health questionnaires using touchscreen computer technology are an accurate means of collecting patient information in the preoperative setting and can provide a valuable basis for an electronic perioperative patient record. Patients expressed comfort and satisfaction with this method of questionnaire completion.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".