Artificial Intelligence Predicts Health-Related Quality of Life for Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
Abstract Purpose Adolescent idiopathic scoliosis (AIS) has a large impact on health-related quality of life (HRQoL) including poor psychosocial functioning, body image, and psychological distress. Surgical management for AIS is common; however, there is limited consensus on preoperative and intraoperative strategies to optimize HRQoL outcomes. Accurate prediction of postoperative outcomes can help guide operative planning and lead to improved HRQoL. This study aimed to generate machine learning models (MLMs) using preoperative and intraoperative variables to predict the difference in HRQoL outcomes from preoperative assessment to two years following AIS surgery. Methods A prospective, longitudinal, multicenter database was queried for AIS patients of Lenke 1 or 5 classification with two-year follow-up. MLMs were generated using preoperative and intraoperative variables to predict the difference in Scoliosis Research Society-22 (ΔSRS-22) questionnaire scores from preoperative assessment to two-year follow-up. MLMs were compared to a model that estimates the mean score by evaluating the mean squared error (MSE) and the fraction of times the prediction was within a predesignated value of the actual score (i.e., buffer accuracy). Results A total of 1,477 patients (84.6% female, 75.0% White) were included. The lowest MSE for each ΔSRS-22 outcome ranged from 0.18–0.48, while the highest 0.25-buffer, 0.5-buffer, 0.75-buffer, and 1-buffer accuracies for each ΔSRS-22 outcome ranged from 34.8%–53.4%, 56.8%–83.1%, 75.0%–94.6%, and 87.2%–97.3%, respectively. These MSEs and buffer accuracies outperformed mean estimates. Conclusion MLMs built using preoperative and intraoperative variables enabled prediction of the difference in HRQoL outcomes from preoperative assessment to two years following AIS surgery. Findings provide key insights into the feasibility of implementing MLMs to guide operative planning and counsel patients on expected outcomes of surgical management. Future work should implement additional surgeon, institution, and patient factors as model predictors to increase predictive accuracy of HRQoL outcomes and ultimately improve individualized patient care through data-driven surgical planning.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".