Klippel-Feil Syndrome: Pathogenesis, Diagnosis, and Management
Bibliographic record
Abstract
Klippel-Feil syndrome (KFS), or congenital fusion of the cervical vertebrae, has been thought to be an extremely rare diagnosis. However, recent literature suggests an increased prevalence, with a high proportion of asymptomatic individuals. Occurring as a sporadic mutation or associated with several genes, the pathogenesis involves failure of cervical somite segmentation and differentiation during embryogenesis. Most commonly, the C2-C3 and C5-C6 levels are involved. KFS is associated with other orthopaedic conditions including Sprengel deformity, congenital scoliosis, and cervical spine abnormalities, as well as several visceral pathologies. There are several classification systems, some based on the anatomic levels of fusion and others on its genetic inheritance. Management of patients with KFS primarily involves observation for asymptomatic individuals. Surgical treatment may be for neurologic complaints, correction of deformity, concomitant spinal anomalies, or for associated conditions and varies significantly. Participation in sports is an important consideration. Recommendations for contact sports or activities depend on both the level and the number of vertebrae involved in the fusion. A multidisciplinary team should be involved in the treatment plan and recommendations for complex presentations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".