Health care utilization by persons with chronic back problems in Canada from 2001 to 2016: A population-based study
Bibliographic record
Abstract
Methods: The NIS database was reviewed from 2005 to 2013.Patients undergoing elective lumbar fusion surgery via an anterior approach were identified by ICD-9 code.Indications specific to the lumbar spine, such as lumbar degenerative disc disease, herniated disc, stenosis, or radiculopathy were also identified by their respective ICD-9 codes.Demographics and Charlson Comorbidity Index (CCI) were assessed.Frequencies of complications including PE, DVT, infection, cardiac, hematoma, durotomy, and mortality were also analyzed.Statistical analysis involved T tests, χ 2 analysis, and binary logistic regression with p<0.001 denoting significance.Results: We identified 27,912 patients which represented an estimated 137,928 of weighted patients hospitalized for primary anterior lumbar spine fusion.Patients undergoing anterior lumbar fusion had a mean age of 55.4 (SDÆ14.3)with 55.3% female patients.The majority of patients were white (82.2%), privately insured (50.4%), had a median household income of $48,000-62,999 (27.1%) and had a CCI score of 0.44 (SDÆ0.8).The majority of procedures were performed in the South region (39.5%), in non-teaching hospitals (54.5%), in hospitals that were private or not-for-profit(70.4%).Bone morphogenic protein (BMP) was used in 43.4% of cases.The most common complication was durotomy (1.2%) followed by cardiac complications and hematoma (0.7%) with the least common being PE (0.1%).The mortality rate was 0.2% for this procedure.Conclusion: This study provides valuable data on the patient demographics and complications of ALIF across the United States.ALIF has a low complication and mortality rate.We expect, with the advancements in instrumentation, success rate of fusions, and an aging population, that anterior fusion will continue to see an increase in utilization by spine surgeons across the country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".