Is Larger Scoliosis Curve Magnitude Associated with Increased Perioperative Health-Care Resource Utilization?
Bibliographic record
Abstract
BACKGROUND: The treatment of patients with large adolescent idiopathic scoliosis curves has been associated with increased surgical complexity. The purpose of this study was to determine whether surgical correction of larger adolescent idiopathic scoliosis curves increased the utilization of health-care resources and to identify potential predictors associated with increased perioperative health-care resource utilization. METHODS: A nested cohort of patients with adolescent idiopathic scoliosis with Lenke type 1A and 1B curves were identified from a prospective longitudinal multicenter database. Four perioperative outcomes were selected as the primary health-care resource utilization outcomes of interest: operative time, number of vertebral levels instrumented, duration of hospitalization, and allogeneic blood transfusion. The effect of curve magnitude on these outcomes was assessed with use of univariate and multivariate regression. RESULTS: Three hundred and twenty-five patients with a mean age of 15 ± 2 years were included. The mean main thoracic curve was 54.4° ± 7.8°. Larger curves were associated with longer operative time (p = 0.03), a greater number of vertebral levels instrumented (p = 0.0005), and the need for blood transfusion (with every 10° increase associated with 1.5 times higher odds of receiving a transfusion). In addition to curve magnitude, surgical center, bone graft method, and upper and lower instrumented levels were strong predictors of operative time (R2 = 0.76). The duration of hospitalization was influenced by the surgical center and intraoperative blood loss (R2 < 0.4), whereas the number of levels instrumented was influenced by the curve magnitude, curve correction percentage, upper instrumented vertebra, and surgical center (R2 = 0.64). CONCLUSIONS: Correction of larger curves was associated with increased utilization of perioperative health-care resources, specifically longer operative time, a greater number of vertebral levels instrumented, and higher odds of receiving a blood transfusion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".