Multi-domain biopsychosocial postoperative recovery trajectories associate with patient outcomes following lumbar fusion
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to describe and assess the impact of multi-domain biopsychosocial (BPS) recovery on outcomes following lumbar spine fusion. We hypothesized that discrete patterns of BPS recovery (e.g., clusters) would be identified, and then associated with postoperative outcomes and preoperative patient data. METHODS: Patient-reported outcomes for pain, disability, depression, anxiety, fatigue, and social roles were collected at multiple timepoints for patients undergoing lumbar fusion between baseline and one year. Multivariable latent class mixed models assessed composite recovery as a function of (1) pain, (2) pain and disability, and (3) pain, disability, and additional BPS factors. Patients were assigned to clusters based on their composite recovery trajectories over time. RESULTS: Using all BPS outcomes from 510 patients undergoing lumbar fusion, three multi-domain postoperative recovery clusters were identified: Gradual BPS Responders (11%), Rapid BPS Responders (36%), and Rebound Responders (53%). Modeling recovery from pain alone or pain and disability alone failed to generate meaningful or distinct recovery clusters. BPS recovery clusters were associated with number of levels fused and preoperative opioid use. Postoperative opioid use (p < 0.01) and hospital length of stay (p < 0.01) were associated with BPS recovery clusters even after adjusting for confounding factors. CONCLUSION: This study describes distinct clusters of recovery following lumbar spine fusion derived from multiple BPS factors, which are related to patient-specific preoperative factors and postoperative outcomes. Understanding postoperative recovery trajectories across multiple health domains will advance our understanding of how BPS factors interact with surgical outcomes and could inform personalized care plans.
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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".