Developing a surgeon‐patient communication checklist for total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty (TKA) is a well-established surgical procedure with high rates of clinical success, yet up to 20% of patients are dissatisfied with their outcomes. Surgeon-patient communication is an important contributor to patient satisfaction, particularly in the area of setting or resetting postoperative expectations that are reasonable and achievable for individual patients. OBJECTIVE: The goal of the present research was to develop a communication checklist for surgeons to use with their patients, to enhance communication in order better to manage postoperative expectations and increase patient satisfaction with TKA. METHODS: Content analysis was used to identify a preliminary list of checklist items from audio-recorded interviews of eight patients who were between 6 weeks and 6 months of their recovery from TKA. Patients identified eight issues for which more information was desired. The proportion of mentions for each topic was calculated, and chi-square tests assessed the distribution and pattern of the categories mentioned across groups of patients. RESULTS: Certain topics were used significantly more often than others. Age and employment status, but not gender, affected which topics were mentioned more often. The final categories for the checklist included pain management, physiotherapy, medication and general outcome information. CONCLUSION: An in-depth analysis of patient experiences of recovering from TKA was the foundation of this research. The result was a checklist that creates a potential new avenue for increasing patient satisfaction by improving surgeon-patient communication. The research described here could extend to any other type of intervention in which understanding patient expectations and increasing patient satisfaction is the goal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".