Comparisons of surgeon and patient prediction of 1-year outcomes following rotator cuff surgery
Bibliographic record
Abstract
Background Preoperative patient expectations are thought to be predictors of outcomes in rotator cuff repair (RCR) surgery; however, these expectations lack an objective component. Surgeons are thought to possess the ability to more accurately predict postoperative patient outcomes compared to patients themselves. Surgeon's predictions of outcome could potentially serve to set realistic patient expectations of surgery and thus optimize surgical outcomes. The objective of this study was to describe patient and surgeon predictions of outcomes in those undergoing RCR surgery and determine the degree of agreement between these predictions and actual one-year postoperative outcomes. Methods Data for this study were collected in a healthcare registry as standard of care for all patients undergoing RCR from January 1 to December 31, 2022 at a single surgical center. Surgeries were conducted by fellowship-trained upper extremity surgeons. The primary outcome was the SANE (Single-Assessment Numeric Evaluation) score which required a written response by the patient to the question, "How would you rate your affected shoulder today as a percentage of normal (0% to 100% with 100% being normal)?". Preoperatively, patients completed their current SANE score and the SANE score they predicted to have at one-year postoperatively. The surgeon was asked to predict the patient's one-year SANE score immediately following completion of the surgery. A repeated-measures ANOVA compared surgeon-predicted, patient-predicted, and actual mean one-year postoperative SANE. Tukey's post hoc test was used to adjust for pairwise comparisons. The differences between actual and surgeon predicted SANE and actual and patient predicted SANE were calculated. "Accurate prediction" was defined a priori as a difference being within +/-5% of the actual SANE. Significance level was set to p <0.05. Results Sixty-nine patients were included in this study with a mean age of 60.9 (SD=6.9) years and 77% (N=53) were male. Surgeon-predicted one-year postoperative SANE was 77.6% (Min=60%; Max=95%; SD=6.8%), patient-predicted was 88.7% (Min=50%; Max=100%; SD=11.1%), and actual SANE was 80.6% (Min=8%; Max=100%; SD=19%) (p<0.001). Mean patient-predicted scores were significantly higher than both mean surgeon-predicted (p<0.001) and mean actual postoperative SANE (p=0.002). There was no statistical difference between mean surgeon-predicted and mean actual postoperative SANE. Conclusion On average, surgeons were better able to predict outcomes compared to patients. Patients tended to overestimate their outcomes, while surgeons tended to underestimate patient outcomes. These findings raise the importance of preoperative patient counselling to set more realistic expectations that would potentially improve their achieved outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.030 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".