The Deformity Angular Ratio
Bibliographic record
Abstract
STUDY DESIGN: A retrospective analysis. OBJECTIVE: The purpose of this study was to determine whether the deformity angular ratio (DAR) can reliably assess the neurological risks of patients undergoing deformity correction. SUMMARY OF BACKGROUND DATA: Identifying high-risk patients and procedures can help ensure that appropriate measures are taken to minimize neurological complications during spinal deformity corrections. Subjectively, surgeons look at radiographs and evaluate the riskiness of the procedure. However, 2 curves of similar magnitude and location can have significantly different risks of neurological deficit during surgery. Whether the curve spans many levels or just a few can significantly influence surgical strategies. Lenke et al have proposed the DAR, which is a measure of curve magnitude per level of deformity. METHODS: The data from 35 pediatric spinal deformity correction procedures with thoracic 3-column osteotomies were reviewed. Measurements from preoperative radiographs were used to calculate the DAR. Binary logistic regression was used to model the relationship between DARs (independent variables) and presence or absence of an intraoperative alert (dependent variable). RESULTS: In patients undergoing 3-column osteotomies, sagittal curve magnitude and total curve magnitude were associated with increased incidence of transcranial motor evoked potential changes. Total DAR greater than 45° per level and sagittal DAR greater than 22° per level were associated with a 75% incidence of a motor evoked potential alert, with the incidence increasing to 90% with sagittal DAR of 28° per level. CONCLUSION: In patients undergoing 3-column osteotomies for severe spinal deformities, the DAR was predictive of patients developing intraoperative motor evoked potential alerts. Identifying accurate radiographical, patient, and procedural risk factors in the correction of severe deformities can help prepare the surgical team to improve safety and outcomes when carrying out complex spinal corrections. LEVEL OF EVIDENCE: 3.
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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".