External Validation of the Adult Spinal Deformity (ASD) Frailty Index (ASD-FI) in the Scoli-RISK-1 Patient Database
Bibliographic record
Abstract
STUDY DESIGN: Analysis of a prospective multicenter database. OBJECTIVE: To assess the ability of the recently created Adult Spinal Deformity (ASD) Frailty Index (ASD-FI) to predict odds of major complications and length of hospital stay for patients who had more severe preoperative deformity and underwent more invasive ASD surgery compared with patients in the database used to create the index. SUMMARY OF BACKGROUND DATA: Accurate preoperative estimates of risk are necessary given the high complication rates currently associated with ASD surgery. METHODS: Patients were enrolled by participating institutions in Europe, Asia, and North America from 2009 to 2011. ASD-FI scores were used to classify 267 patients as not frail (NF) (<0.3), frail (0.3-0. 5), or severely frail (SF) (>0.5). Multivariable logistic regression, adjusted for preoperative and surgical covariates such as operative time and blood loss, was performed to determine the relationship between ASD-FI category and incidence of major complications, overall incidence of complications, and length of hospital stay. RESULTS: The mean ASD-FI score was 0.3 (range, 0-0.7). We categorized 105 patients as NF, 103 as frail, and 59 as SF. The adjusted odds of developing a major complication were higher for SF patients (odds ratio = 4.4; 95% CI 2.0, 9.9) compared with NF patients. After adjusting for covariates, length of hospital stay for SF patients increased by 19% (95% CI 1.4%, 39%) compared with NF patients. The odds of developing a major complication or having increased length of stay were similar between frail and NF patients. CONCLUSION: Greater patient frailty, as measured by the ASD-FI, is associated with a longer hospital stay and greater risk of major complications among patients who have severe preoperative deformity and undergo invasive surgical procedures. LEVEL OF EVIDENCE: 2.
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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".