Decisional Regret Among Older Adults Undergoing Corrective Surgery for Adult Spinal Deformity: A Single Institutional Study
Bibliographic record
Abstract
STUDY DESIGN: Retrospective. OBJECTIVE: To investigate the prevalence of decisional regret among older adults undergoing surgery for adult spinal deformity (ASD). SUMMARY OF BACKGROUND DATA: Among older adults (≥65 years old), ASD is a leading cause of disability, with a population prevalence of 60% to 70%. While surgery is beneficial and results in functional improvement, in over 20% of older adults outcomes from surgery are less desirable. METHODS: Older adults with ASD who underwent spinal surgery at a quaternary medical center from January 1, 2016 to March 1, 2019, were enrolled in this study. Patients were categorized into medium/high or low-decisional regret cohorts based on their responses to the Ottawa decision regret questionnaire. Decisional regret assessments were completed 24 months after surgery. The primary outcome measure was prevalence of decisional regret after surgery. Factors associated with high decisional regret were analyzed by multivariate logistic regression. RESULTS: A total of 155 patients (mean age, 69.5 yrs) met the study inclusion criteria. Overall, 80% agreed that having surgery was the right decision for them, and 77% would make the same choice in future. A total of 21% regretted the choice that they made, and 21% responded that surgery caused them harm. Comparing patient cohorts reporting medium/high- versus low-decisional regret, there were no differences in baseline demographics, comorbidities, invasiveness of surgery, length of stay, discharge disposition, or extent of functional improvement 12-months after surgery. After adjusting for sex, American Society of Anesthesiologists score, invasiveness of surgery, and presence of a postoperative complication, older adults with preoperative depression had a 4.0 fold increased odds of high-decisional regret (P = 0.04). Change in health related quality of life measures were similar between all groups at 12-months after surgery. CONCLUSION: While the majority of older adults were appropriately counseled and satisfied with their decision, one-in-five older adults regret their decision to undergo surgery. Preoperative depression was associated with medium/high decisional regret on multivariate analysis.Level of Evidence: 4.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".