Reliability Assessment of a Novel Cervical Deformity Classification System
Bibliographic record
Abstract
Introduction Despite the complexity of cervical deformity and the significant impact on patient quality of life, there exists no comprehensive classification. An initial novel classification system has been recently designed with a deformity descriptor and five modifiers that incorporate sagittal regional and global spino-pelvic alignment and neurological status. Our objective was to characterize the intra- and interobserver reliability of this classification. Material and Methods A series of 10 cervical deformity cases, broadly representative of the classification system, were selected and sufficient radiographic and clinical history to enable classification were assembled. A panel of deformity surgeons was queried to classify each case twice, with a minimum of 1 intervening week. Inter- and intrarater reliability measures were based on calculations of Fleiss kappa coefficient values. Results A total of 20 spine deformity surgeons participated in this study. Interrater reliability (Fleiss kappa coefficients) for the deformity descriptor rounds 1 and 2 were 0.489 and 0.280, respectively, and mean intrarater reliability was 0.584. For the modifiers, including the SRS–Schwab components, the interrater (round 1/round 2) and intrarater reliabilities (Fleiss kappa coefficients) were as follows: C2-C7 SVA (0.338/0.412, 0.584), horizontal gaze (0.779/0.430, 0.768), TS–CL (0.721/0.567, 0.720), myelopathy (0.602/0.477, 0.746), curve type (0.590/0.433, 0.564), PI–LL (0.554/0.386, 0.826), PT (0.714/0.627, 0.633), and C7–S1 SVA (0.071/0.064, 0.233), respectively. The parameter with the poorest reliability was the C7–S1 SVA, which may have resulted from differences in interpretation of positive and negative measurements. Conclusion The proposed classification provides a mechanism to assess cervical deformity within the framework of global spino-pelvic malalignment and clinically relevant parameters. The intra- and interobserver reliabilities suggest moderate agreement and serve as the basis for subsequent improvement and study of the proposed classification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".