Standardized Questionnaire Time Burden for Practitioners and Patients.
Bibliographic record
Abstract
INTRODUCTION: Many questionnaires are used to assess patient-reported outcomes, but there are few studies assessing the time to complete these questionnaires. The purpose of this study was to: (1) evaluate how much time it takes to complete the most commonly used patient-reported outcome questionnaires; (2) calculate the potential variation for time of completion; and (3) assess the potential role of demographics. MATERIALS AND METHODS: After literature review, nine different questionnaires were chosen based on the frequency of citation. Each patient was given one questionnaire and time to complete was recorded. Mean times were compared and statistical analysis was performed on patients based on age≥55 years, gender, and education level. RESULTS: The mean time of completion for each questionnaire is listed from shortest to longest: University of California Los Angeles (UCLA) activity score, Lower Extremity Activity Scale (LEAS), Hospital for Special Surgery Score (HSS), Lower Extremity Functional Scale (LEFS), Oxford Knee Score-12 (OKS-12), Knee Society Scores (KSS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), Short Form-36 (SF-36), and the Knee Injury and Osteoarthritis Outcome Score (KOOS). The coefficients of variation were smallest in SF-36 and WOMAC while it was the largest in the UCLA activity score. Age of ≥55 years was associated with a longer time to complete the questionnaires. There was no association found between gender or education level. DISCUSSION: It is possible that if it takes longer to complete certain questionnaires, then the answers given may not accurately reflect the patient's condition. Future studies should focus on the accuracy of the respondents' answers to each questionnaire as well as the accuracy after filling out multiple questionnaires at a single patient office visit.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".