Cervical disc replacement: examining “real-world” utilization of an emerging technology
Bibliographic record
Abstract
OBJECTIVE: Cervical disc replacement (CDR) has emerged as an alternative to anterior cervical discectomy and fusion (ACDF) for the management of cervical spondylotic pathology. While much is known about the efficacy of CDR within the constraints of a well-controlled, experimental setting, little is known about general utilization. The authors present an analysis of temporal and geographic trends in "real-world" utilization of CDR among those enrolled in private insurance plans in the US. METHODS: Eligible subjects were identified from the IBM MarketScan Databases between 2009 and 2017. Individuals 18 years and older, undergoing a single-level CDR or ACDF for cervical radiculopathy and/or myelopathy, were identified. US Census divisions were used to classify the region where surgery was performed. Two-level mixed-effects regression modeling was used to study regional differences in proportional utilization of CDR, while controlling for confounding by regional case-mix differences. RESULTS: A total of 47,387 subjects met the inclusion criteria; 3553 underwent CDR and 43,834 underwent ACDF. At a national level, the utilization of single-level CDR rose from 5.6 cases for every 100 ACDFs performed in 2009 to 28.8 cases per 100 ACDFs in 2017. The most substantial increases occurred from 2013 onward. The region of highest utilization was the Mountain region (Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and Wyoming), where 14.3 CDRs were performed for every 100 ACDFs (averaged over the 9-year period of study). This is in contrast to the East South Central region (Alabama, Kentucky, Mississippi, and Tennessee), where only 2.1 CDRs were performed for every 100 ACDFs. Patient factors that significantly increased the odds of undergoing a CDR were age younger than 40 years (OR 15.9 [95% CI 10.0-25.5]; p < 0.001), no clinical evidence of myelopathy/myeloradiculopathy (OR 1.5 [95% CI 1.4-1.7]; p < 0.001), and a Charlson Comorbidity Index score of 0 (OR 2.7 [95% CI 1.7-4.2]; p < 0.001). After controlling for these factors, significant differences in utilization rates remained between regions (chi-square test = 830.4; p < 0.001). CONCLUSIONS: This US national level study lends insight into the rate of uptake and geographic differences in utilization of the single-level CDR procedure. Further study will be needed to ascertain specific factors that predict adoption of this technology to explain observed geographic discrepancies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".