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Record W3195417533 · doi:10.3171/2021.2.spine202150

National adverse event profile after lumbar spine surgery for lumbar degenerative disorders and comparison of complication rates between hospitals: a CSORN registry study

2021· article· en· W3195417533 on OpenAlexaffabout
Oliver G. S. Ayling, Raphaële Charest-Morin, Matthew E. Eagles, Tamir Ailon, John Street, Nicolas Dea, Greg McIntosh, Sean Christie, Edward Abraham, W. Bradley Jacobs, Christopher S. Bailey, Michael G. Johnson, Najmedden Attabib, Peter Jarzem, Michael H. Weber, Jérôme Paquet, Joel Finkelstein, Alexandra Stratton, Hamilton Hall, Neil Manson, Y. Raja Rampersaud, Kenneth Thomas, Charles G. Fisher

Bibliographic record

VenueJournal of Neurosurgery Spine · 2021
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversité LavalUniversity of ManitobaWestern UniversityUniversity of TorontoDalhousie UniversityUniversity of OttawaCanada East Spine CentreMcGill UniversityUniversity of CalgaryVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineAdverse effectProspective cohort studyIncidence (geometry)ComplicationSurgeryLumbarInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Previous works investigating rates of adverse events (AEs) in spine surgery have been retrospective, with data collection from administrative databases, and often from single centers. To date, there have been no prospective reports capturing AEs in spine surgery on a national level, with comparison among centers. METHODS: The Spine Adverse Events Severity system was used to define the incidence and severity of AEs after spine surgery by using data from the Canadian Spine Outcomes and Research Network (CSORN) prospective registry. Patient data were collected prospectively and during hospital admission for those undergoing elective spine surgery for degenerative conditions. The Spine Adverse Events Severity system defined minor and major AEs as grades 1-2 and 3-6, respectively. RESULTS: There were 3533 patients enrolled in this cohort. There were 85 (2.4%) individual patients with at least one major AE and 680 (19.2%) individual patients with at least one minor AE. There were 25 individual patients with 28 major intraoperative AEs and 260 patients with 275 minor intraoperative AEs. Postoperatively there were 61 patients with a total of 80 major AEs. Of the 487 patients with minor AEs postoperatively there were 698 total AEs. The average enrollment was 321 patients (range 47-1237 patients) per site. The rate of major AEs was consistent among sites (mean 2.9% ± 2.4%, range 0%-9.1%). However, the rate of minor AEs varied widely among sites-from 7.9% to 42.5%, with a mean of 18.8% ± 9.7%. The rate of minor AEs varied depending on how they were reported, with surgeon reporting associated with the lowest rates (p < 0.01). CONCLUSIONS: The rate of major AEs after lumbar spine surgery is consistent among different sites but the rate of minor AEs appears to vary substantially. The method by which AEs are reported impacts the rate of minor AEs. These data have implications for the detection and reporting of AEs and the design of strategies aimed at mitigating complications.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.358
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2021
Admission routes2
Has abstractyes

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