The Influence of Surgical Intervention and Sagittal Alignment on Frailty in Adult Cervical Deformity
Bibliographic record
Abstract
BACKGROUND: Frailty is a relatively new area of study for patients with cervical deformity (CD). As of yet, little is known of how operative intervention influences frailty status for patients with CD. OBJECTIVE: To investigate drivers of postoperative frailty score and variables within the cervical deformity frailty index (CD-FI) algorithm that have the greatest capacity for change following surgery. METHODS: Descriptive analysis of the cohort were performed, paired t-tests determined significant baseline to 1 yr improvements of factors comprising the CD-FI. Pearson bivariate correlations identified significant associations between postoperative changes in overall CD-FI score and CD-FI score components. Linear regression models determined the effect of successful surgical intervention on change in frailty score. RESULTS: A total of 138 patients were included with baseline frailty scores of 0.44. Following surgery, mean 1-yr frailty score was 0.27. Of the CD-FI variables, 13/40 (32.5%) were able to improve with surgery. Frailty improvement was found to significantly correlate with baseline to 1-yr change in CBV, PI-LL, PT, and SVA C7-S1. HRQL CD-FI components reading, feeling tired, feeling exhausted, and driving were the greatest drivers of change in frailty. Linear regression analysis determined successful surgical intervention and feeling exhausted to be the greatest significant predictors of postoperative change in overall frailty score. CONCLUSION: Complications, correction of sagittal alignment, and improving a patient's ability to read, drive, and chronic exhaustion can significantly influence postoperative frailty. This analysis is a step towards a greater understanding of the relationship between disability, frailty, and surgery in CD.
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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 | 0.005 |
| 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) | 0.002 | 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".