The Health Impact of Adult Cervical Deformity in Patients Presenting for Surgical Treatment: Comparison to United States Population Norms and Chronic Disease States Based on the EuroQuol-5 Dimensions Questionnaire
Bibliographic record
Abstract
BACKGROUND: Although adult cervical spine deformity (ACSD) is associated with pain and disability, its health impact has not been quantified in comparison to other chronic diseases. OBJECTIVE: To perform a comparative analysis of the health impact of symptomatic ACSD to US normative and chronic disease values using EQ-5D (EuroQuol-5 Dimensions questionnaire) scores. METHODS: ACSD patients presenting for surgical treatment were identified from a prospectively collected multicenter database. Baseline demographics and EQ-5D scores were collected and compared with US normative and disease state values. RESULTS: Of 121 ACSD patients, 115 (95%) completed the EQ-5D (60% women, mean age 61 years, previous spine surgery in 44%). Diagnoses included kyphosis with mid-cervical (63.4%), cervico-thoracic (23.5%), or thoracic (8.7%) apex and primary coronal deformity (4.3%). The mean ACSD EQ-5D index was 0.511 (standard definition = 0.224), which is 34% below the bottom 25th percentile (0.780) for similar age- and gender-matched US normative populations. Mean ACSD EQ-5D index values were worse than the bottom 25th percentile for several other disease states, including chronic ischemic heart disease (0.708), malignant breast cancer (0.708), and malignant prostate cancer (0.708). ACSD mean index values were comparable to the bottom 25th percentile values for blindness/low vision (0.543), emphysema (0.508), renal failure (0.506), and stroke (0.463). EQ-5D scores did not significantly differ based on cervical deformity type ( P = .66). CONCLUSION: The health impact of symptomatic ACSD is substantial, with negative impact across all EQ-5D domains. The mean ACSD EQ-5D index was comparable to the bottom 25th percentile values for blindness/low vision, emphysema, renal failure, and stroke.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".