Measuring Expectations in Orthopaedic Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Advances in the surgical treatment of musculoskeletal conditions have resulted in an interest in better defining and understanding patients' expectations of these procedures, but the best ways to do this remain a topic of considerable debate. QUESTIONS/PURPOSES: (1) What validated instruments for the assessment of patient expectations of orthopaedic surgery have been used in published studies to date? (2) How were these expectation measures developed and validated? (3) What unvalidated instruments for the assessment of patient expectations have been used in published studies to date? METHODS: A systematic literature search was performed using the OVID Medline and EMBASE databases, in duplicate, to identify all studies that assessed patient expectations in orthopaedic surgery. Sixty-six studies were ultimately included in the present review. RESULTS: Seven validated expectation instruments were identified, all of which use patient-reported questionnaires. Five were specific to a particular procedure or affected anatomic location, whereas two were broadly applicable. Details of reliability and validity testing were available for all but one of these instruments. Forty additional unvalidated expectation assessment tools were identified. Thirteen were based on existing clinical outcome tools, and the others were study-specific, custom-developed tools. Only one of the unvalidated tools was used in more than one study. CONCLUSIONS: Several validated expectation instruments have been developed for use by patients undergoing orthopaedic surgery. However, many tools have been reported without evidence of testing and validation. The wide range of untested instruments used in single studies substantially limits the interpretation and comparison of data concerning patient expectations.
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".