Klippel Feil Syndrome
Bibliographic record
Abstract
STUDY DESIGN: Retrospective study. OBJECTIVE: To define distinct Klippel-Feil syndrome (KFS) patient phenotypes that are associated with the need for surgical intervention. SUMMARY OF BACKGROUND DATA: KFS is characterized by the congenital fusion of cervical vertebrae; however, patients often present with a variety of other spinal and extraspinal anomalies suggesting this syndrome encompasses a heterogeneous patient population. Moreover, it remains unclear how the abnormalities seen in KFS correlate to neurological outcomes and the need for surgical intervention. METHODS: Principal component (PC) analysis was performed on 132 KFS patients treated at a large pediatric hospital between 1981 and 2018. Thirty-five variables pertaining to patient/disease-related factors were examined. Significant PCs were included as independent variables in multivariable logistic regression models designed to test associations with three primary outcomes: cervical spine surgery, thoracolumbar/sacral spine surgery, and cranial surgery. RESULTS: Fourteen significant PCs accounting for 70% of the variance were identified. Five components, representing four distinct phenotypes, were significantly associated with surgical intervention. The first group consisted of predominantly subaxial cervical spine fusions, thoracic spine abnormalities and was associated with thoracolumbar/sacral spine surgery. The second group was largely represented by axial cervical spine anomalies and had high association with cervical subluxation and cervical spine surgery. A third group, heavily represented by Chiari malformation, was associated with cranial surgery. Lastly, a fourth group was defined by thoracic vertebral anomalies and associations with sacral agenesis and scoliosis. This phenotype was associated with thoracolumbar/sacral spine surgery. CONCLUSION: This is the first data-driven analysis designed to relate KFS patient phenotypes to surgical intervention and provides important insight that may inform targeted follow-up regimens and surgical decision-making. LEVEL OF EVIDENCE: 3.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".