Factors affecting response rates to mailed preoperative surveys among arthroplasty patients
Bibliographic record
Abstract
AIM: To identify factors that affect patient response rates to preoperative functional surveys in hip and knee arthroplasty patients. METHODS: From May 2008 to March 2009, 247 patients were scheduled more than 4 wk in advance for hip or knee arthroplasty by one of two participating surgeons at our center. A personalized questionnaire comprised of the Short Form 12 (SF-12) and Western Ontario and McMaster Universities (WOMAC) Index was mailed to patients at random time points ranging from 7 to 101 d prior to surgery. Nine independent factors were documented prospectively, including age, gender, ethnicity, marital status, type of surgery, surgeon, days prior to surgery (DPS) of survey mailing, WOMAC score and SF-12 score. The date of the completed survey receipt was also documented. For non-responders, the surveys were completed with the research team at the hospital upon admission. Multivariate regression and χ(2) analysis were performed with Statistical Analysis Software software. RESULTS: DPS was the only factor that affected patient response. Mailing surveys 26 d to 31 d prior to surgery dates led to a peak response rate of 80% that was significantly higher (P < 0.023) than response rates for patients who were mailed their surveys ≤ 16 d (62.5%), 17 d to 25 d (70%) or ≥ 32 d prior to surgery (55%). No other factors, including preoperative WOMAC and SF-12 scores, significantly influenced response behavior. CONCLUSION: The DPS was independently the most significant predictor of response rates for pre-operative functional data among patients scheduled for hip and knee arthroplasty.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".