Current Trends in Demographics, Practice, and In-Hospital Outcomes in Cervical Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Retrospective database analysis. OBJECTIVE: To investigate national trends of cervical spine surgical procedures from 2002 to 2011. SUMMARY OF BACKGROUND DATA: There is a paucity of literature assessing the current practice trends and outcomes of cervical spine surgery following the 2008 Food and Drug Administration public health notifications regarding bone morphogenetic protein (BMP) utilization in cervical spine surgical procedures. METHODS: The National Inpatient Sample database was accessed for each year across 2002 to 2011. Patients undergoing anterior cervical fusion, posterior cervical fusion, and posterior cervical decompression were identified. Patient and hospitalization parameters including demographics, BMP utilization, costs, early postoperative outcomes, and mortality were assessed for each surgical cohort. A Pearson correlation coefficient with a 95% confidence interval (P < 0.05) was used to analyze trends in patient and hospital outcome parameters during this 10-year period. RESULTS: A total of 307,188 cervical spine procedures were performed from 2002 to 2011. Both the anterior cervical fusion and posterior cervical fusion cohort demonstrated a statistically significant increase in the number of procedures performed over time (r = +0.9, P < 0.001). A significant uptrend in patient age (r = +1.0, P < 0.001) and comorbidity burden (r = +0.9, P < 0.001) was demonstrated during the studied decade. Overall, BMP utilization (r = +0.7, P = 0.02) also demonstrated a significant increase during this time period, but demonstrated a decline after peaking in 2007. The posterior cervical fusion cohort demonstrated the greatest comorbidity, length of stay, costs, and mortality. CONCLUSION: This study demonstrates that the number of cervical spine procedures has increased between 2002 and 2011, irrespective of the change in BMP utilization after the 2008 Food and Drug Administration warning. Despite an older patient population with greater comorbidities undergoing cervical spine surgeries, hospital length of stay and mortality has not significantly changed. However, we did note a significant increase in costs during this time period. These findings may be related to advances in surgical technology and instrumentation that may be associated with rising hospital costs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".