A Systematic Review of Classification Systems for Cervical Ossification of the Posterior Longitudinal Ligament
Bibliographic record
Abstract
DESIGN: Systematic review. OBJECTIVE: To conduct a systematic review to (1) summarize various classification systems used to describe cervical ossification of the posterior longitudinal ligament (OPLL) and (2) evaluate the diagnostic accuracy of various imaging modalities and the reliability of these classification systems. METHODS: A search was performed to identify studies that used a classification system to categorize patients with OPLL. Furthermore, studies were included if they reported the diagnostic accuracy of various imaging modalities or the reliability of a classification system. RESULTS: A total of 167 studies were deemed relevant. Five classification systems were developed based on X-ray: the 9-classification system (0.60%); continuous, segmental, mixed, localized or focal, circumscribed and others (92.81%); hook, staple, bridge, and total types (2.40%); distribution of OPLL (2.40%); and K-line classification (4.19%). Six methods were based on computed tomography scans: free-type, contiguous-type, and broken sign (0.60%); hill-, plateau-, square-, mushroom-, irregular-, or round-shaped (5.99%); rectangular, oval, triangular, or pedunculate (1.20%); centralized or laterally deviated (1.80%); plank-, spindle-, or rod-shaped (0.60%); and rule of nine (0.60%). Classification systems based on 3-dimensional computed tomography were bridging and nonbridging (1.20%) and flat, irregular, and localized (0.60%). A single classification system was based on magnetic resonance imaging: triangular, teardrop, or boomerang. Finally, a variation of methods was used to classify OPLL associated with the dura mater (4.19%). CONCLUSIONS: The most common method of classification was that proposed by the Japanese Ministry of Health, Labour and Welfare. Other important methods include K-line (+/-), signs of dural ossification, and patterns of distribution.
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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 | 0.021 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".