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Record W4392767199 · doi:10.3171/2024.1.spine23621

Differences in the surgical management of degenerative lumbar spondylolisthesis based on self-reported sex: analysis of the CSORN prospective DLS study

2024· article· en· W4392767199 on OpenAlexaffabout
Jennifer Urquhart, Parham Rasoulinejad, Supriya Singh, Andrew Glennie, Charles G. Fisher, Y. Raja Rampersaud, Edward Abraham, John Street, Marcel F. Dvorak, Scott Paquette, Raphaële Charest-Morin, Nicolas Dea, Nicholas Gélinas-Phaneuf, Brian Kwon, Christopher S. Bailey

Bibliographic record

VenueJournal of Neurosurgery Spine · 2024
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsHôpital de l'Enfant-JésusCanada East Spine CentreVancouver Spine Surgery InstituteUniversity Health NetworkDalhousie UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineDecompressionSpondylolisthesisSurgeryRadiographyLumbarCohortPropensity score matchingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Surgical treatment of degenerative lumbar spondylolisthesis (DLS) reliably improves patient-reported quality of life; however, patient population heterogeneity, in addition to other factors, ensures ongoing equipoise in choosing the ideal surgical treatment. Surgeon preference for fusion or decompression alone influences surgical treatment decision-making. Meanwhile, at presentation, patient-reported outcome measures (PROMs) differ considerably between females and males. The aims of this study were to determine whether there exists a difference in the rates of decompression and fusion versus decompression alone based on patient-reported sex, and to determine if widely accepted indications for fusion justify any observed differences or if surgeon preference plays a role. METHODS: This study is a retrospective cohort analysis of patients enrolled in the Canadian Spine Outcomes Research Network (CSORN) DLS study, a multicentered Canadian prospective study, investigating the surgical management and outcome of DLS. Decompression and fusion rates, patient characteristics, preoperative PROMs, and radiographic measures were compared between males and females before and after propensity score matching. RESULTS: In the unmatched cohort, female patients were more likely to undergo decompression and fusion than male patients. Females were more likely to have the recognized indications for fusion, including kyphotic disc angle, higher spondylolisthesis grade and slip percentage, and patient-reported back pain. Other radiographic findings associated with the decision to fuse, including facet effusion, facet distraction, or facet angle, were not more prevalent in females. After propensity score matching for demographic and radiographic characteristics, similar proportions of male and female patients underwent decompression and fusion and decompression alone. CONCLUSIONS: Although it remains unclear who should or should not undergo fusion, in addition to surgical decompression of DLS, female patients undergo fusion at a higher rate than their male counterparts. After matching baseline radiographic factors indicating fusion, this analysis showed that the decision to fuse was not biased by sex differences. Rather, the higher proportion of females undergoing fusion is largely explained by the radiographic and clinical indications for fusion, suggesting that specific clinical and anatomical features of this condition are indeed different between sexes.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.079
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.036
GPT teacher head0.314
Teacher spread0.278 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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