A simple scoring system for predicting early major complications in spine surgery: the cumulative effect of age and size of surgery
Bibliographic record
Abstract
OBJECTIVE: To analyze the cumulative effect of risk factors associated with early major complications in postoperative spine surgery. METHODS: Retrospective analysis of 583 surgically-treated patients. Early "major" complications were defined as those that may lead to permanent detrimental effects or require further significant intervention. A balanced risk score was built using multiple logistic regression. RESULTS: Ninety-two early major complications occurred in 76 patients (13%). Age > 60 years and surgery of three or more levels proved to be significant independent risk factors in the multivariate analysis. The balanced scoring system was defined as: 0 points (no risk factor), 2 points (1 factor) or 4 points (2 factors). The incidence of early major complications in each category was 7% (0 points), 15% (2 points) and 29% (4 points) respectively. CONCLUSIONS: This balanced scoring system, based on two risk factors, represents an important tool for both surgical indication and for patient counseling before surgery.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".