Feasibility of collecting routine information for clinical and research purposes via electronic format questionnaire.
Bibliographic record
Abstract
167 Background: Patient demographics, lifestyle factors, and past medical history enable clinicians to optimize care plans, and be useful for health services research. Unfortunately, these data are often recorded inconsistently in the medical chart. In 2015, we tested a paper-form questionnaire to collect such data systematically in the outpatient setting. This current study assessed the feasibility (applicability, acceptability, practicality) of administering the questionnaire via electronic format (iPad). Objectives: To examine whether the electronic format of the questionnaire was (1) applicable (reliable and complete data); (2) acceptable to patients; and (3) practical for clinic utilization. Methods: New adult cancer patients visiting the thoracic cancer clinic at Princess Margaret Cancer Centre in summer, 2016 were asked to complete a Patient Health Questionnaire via iPad devices. This questionnaire was developed through trial testing in addition to literature review, expert opinion, and prior paper-based testing. Results: In 62 new patients (57% male, mean age 66 years old), this electronic questionnaire took on average 25 minutes to complete. The electronic questionnaire was applicable (89% completed the questionnaire, reliable data) and acceptable (69% were happy to complete, 69% found questionnaire useful, 61% thought it asked the right questions, 71% did not think it made clinic visits more difficult). For practicality, although the data were easily interpretable by clinicians, 48% of patients failed to complete the questionnaire before they were seen by their clinicians. Conclusions: Though feasible to collect electronically standardized demographics, lifestyle, and past medical history routinely, timeliness was an issue. Earlier arrival times or completion at home may be necessary to improve clinical utility of the questionnaire.
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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 | 0.099 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".