What Makes a Successful Survey? A Systematic Review of Surveys Used in Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
PURPOSE: To characterize and assess the methodological quality of patient and physician surveys related to anterior cruciate ligament reconstruction, and to analyze the factors influencing response rate. METHODS: The databases MEDLINE, Embase, and PubMed were searched from database inception to search date and screened in duplicate for relevant studies. Data regarding survey characteristics, response rates, and distribution methods were extracted. A previously published list of recommendations for high-quality surveys in orthopaedics was used as a scale to assess survey quality (12 items scored 0, 1, or 2; maximum score = 24). RESULTS: Of the initial 1,276 studies, 53 studies published between 1986 and 2016 met the inclusion criteria. Sixty-four percent of studies were distributed to physicians, compared with 32% distributed to patients and less than 4% to coaches. The median number of items in each survey was 10.5, and the average response rate was 73% (range: 18% to 100%). In-person distribution was the most common method (40%), followed by web-based methods (28%) and mail (25%). Response rates were highest for surveys targeted at patients (77%, P < .0001) and those delivered in-person (94%, P < .0001). The median quality score was 12/24 (range = 8.5/24 to 21/24). There was high inter-rater agreement using the quality scale (intraclass correlation coefficient = 0.92), but there was no correlation with the response rate (Rho = -0.01, P = .97). CONCLUSIONS: Response rates vary based on target audience and distribution methods, with patients responding at a significantly higher rate than physicians and in-person distribution yielding significantly higher response rates than web or mail surveys. LEVEL OF EVIDENCE: Level IV, systematic review of Level IV studies.
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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 | 0.151 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".