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Record W4415570929 · doi:10.1302/1358-992x.2025.11.049

REGIONAL TRENDS IN RATES OF DECOMPRESSION VERSUS FUSION FOR STENOSIS AND DEGENERATIVE SPONDYLOLISTHESIS IN CANADA

2025· article· en· W4415570929 on OpenAlexaffabout
Christopher J. Moran, Andrew Glennie, Lynn Lethbridge, J-A. Douglas

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLaminectomyDecompressionSpondylolisthesisLumbar spinal stenosisSpinal stenosisStenosisPopulationSpinal fusion

Abstract

fetched live from OpenAlex

Recent literature has suggested non-inferiority of decompression alone compared to decompression and fusion for surgical treatment of degenerative conditions of the lumbar spine. Despite this, surgical treatment varies significantly by geography across the United States. Spinal fusion is significantly more costly than decompression alone and analyzing variation between provinces is critical to know where there may be opportunities for cost savings. We will explore geographic and temporal trends in treatment of stenosis and spondylolisthesis in Canada. Retrospective analysis of data from the hospital Discharge Abstract Database from 2007–2018 for 9 of 10 Canadian provinces. Per capita decompression with and without fusion for stenosis and spondylolisthesis by geographical location. The study population was identified using International Classification of Diseases (ICD-10-CA), to select those with a primary or secondary diagnosis for lumbar spinal stenosis and spondylolisthesis. These were stratified into three categories (laminectomy, spinal fusion, or other procedure) using Canadian Classification Codes (CCI) to identify procedures. Patients under 18 years or undergoing outpatient surgery were excluded. Groups were stratified into three-year time periods across five geographic regions. Per capita rates of diagnosis and procedure were calculated. Study demographics and provincial trends were compared using multivariate analysis, and ANOVA for within group differences. A sample population of 86 805 cases were identified (44470 female; mean age 66.23y, SD 13.71). This including 66 740 cases of stenosis, of which 45 570 were treated with laminectomy (25 595, 38.1%) or fusion (19 570, 29.9%). Also identified were 20 065 cases of spondylolisthesis (fusion n=16295, 81.1%; laminectomy n=1135, 5.7%). A large proportion of both groups were treated with a procedure code other than laminectomy or fusion (stenosis n=21170, 31.7%; spondylolisthesis n=2635, 13.1%). Treatment of spinal stenosis varied significantly across the regions. British Columbia (BC) surgeons performed laminectomy rather than fusion throughout the timeframe of the study for spinal stenosis (laminectomy 55.4% vs spine fusion 15.9% in the period 2016–2018). This was contrasted by the Atlantic region, which had similar population incidences of operative stenosis (29.29 per 100 000, vs 28.06 per 100 000 BC), but nearly equal treatment proportions (31.35% laminectomy vs 32.78% fusion). While regional trends were observed, nationally, the rate of decompression procedures did not change in the study period (p=0.76). There was a significant increase in both fusion and other procedure types (p<0.01). Treatment of spondylolisthesis was consistent nationally, with strong preference to fusion (med. proportion 80.49%; rate 0.47 cases per 100 000, +/− 0.01 95%CI). Rates of decompression remained low (0.26 cases per 100 000; +/−0.01 95%CI); BC was an outlier with significantly higher rates of laminectomy alone, which increased over time (7.89%-14.36%; p<0.01). There is significant geographical variation in proportions of decompression and fusion for spinal stenosis but less so for degenerative spondylolisthesis. Fusion rates continue to climb despite a paucity of work demonstrating significant benefit. Future work must focus on identifying the reasons for variance across geographical location given the single payer nature of the Canadian system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.320
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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