Predicting Health-Related Quality of Life Outcomes Following Major Scoliosis Surgery in Adolescents: A Latent Class Growth Analysis
Bibliographic record
Abstract
STUDY DESIGN: Prospective cohort study. OBJECTIVES: To identify patient trajectories of recovery defined by change in health-related quality of life (HRQOL) following surgery for adolescent idiopathic scoliosis (AIS). To explore possible predictors of trajectory membership. METHODS: study. Responses to the Pediatric Quality of Life Inventory-version 4 (PedsQL-4.0) were collected prior to surgery and 4 to 6 weeks, 3, 6, and 12 months post-operatively. Latent class growth analyses identified patient subgroups based on their unique trajectories of physical health (PH) and psychosocial health (PSH) outcomes using the PedsQL-4.0 subscale scores. Predictors included demographic, clinical, and psychosocial factors. RESULTS: Data from up to 190 patients were included (87.4% female; mean±SD age = 14.6 ± 1.9 years). Three trajectory subgroups were identified for PH and 4 trajectories were found for PSH, with a majority of patients scoring within the established range of healthy adolescents 12 months post-surgery. Increased child and parent pain catastrophizing, child trait anxiety and previous hospitalizations were associated with poorer PH outcomes, whereas increased child and parent pain catastrophizing, child state and trait anxiety, and parent state and trait anxiety were associated with poorer PSH trajectories. CONCLUSIONS: The PH and PSH trajectories identified in this study and the factors associated with their membership may inform surgical decision-making for AIS while facilitating patient and family counselling regarding peri-operative recovery and expectations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".