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Record W4385973437 · doi:10.3171/2023.6.spine23461

Adverse effects of frailty on the outcomes of surgery for degenerative cervical myelopathy: results from a prospective multicenter international data set of 757 patients

2023· article· en· W4385973437 on OpenAlexaff
Jamie Wilson, Jetan H. Badhiwala, Ali Moghaddamjou, Alexander R. Vaccaro, Paul M. Arnold, Ronald Bartels, Giuseppe Barbagallo, Michael G. Fehlings

Bibliographic record

VenueJournal of Neurosurgery Spine · 2023
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsToronto Western HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMyelopathyMedicineProspective cohort studyAdverse effectMulticenter studyInternal medicinePsychiatryRandomized controlled trial

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of this study was to determine the effect of the degree of frailty on long-term neurological and functional outcomes after surgery for degenerative cervical myelopathy (DCM). METHODS: A combined database of patients enrolled in the Cervical Spondylotic Myelopathy-North America and Cervical Spondylotic Myelopathy-International prospective international multicenter observational studies who underwent surgery for DCM was used as the source data. All patients underwent baseline and follow-up assessment at 2 years after surgery for functional, disability, and quality of life measurements (modified Japanese Orthopaedic Association [mJOA] scale, Neck Disability Index, SF-36 physical and mental component summary scores). Patients were separated into 4 groups according to their baseline modified frailty index 5-point scale score: not frail, pre-frail, frail, and severely frail. Differences among groups were analyzed at baseline and at 2 years after surgery, including change in scores (delta values) and the odds ratio of achieving the minimum clinically important difference (MCID) through univariate and multivariable logistic regression adjusting for age, approach, number of levels treated, and sex. RESULTS: A total of 757 patients (63% male) with a mean age of 56 (95% CI 55.5-57.2) years were included: 470 patients underwent an anterior approach, 310 had a posterior approach, and 23 had a combined anterior/posterior approach. A total of 50% (n = 378) of patients were classified as not frail, with 33% (n = 250) pre-frail, 13% (n = 101) frail, and 4% (n = 28) severely frail. The baseline mJOA score was significantly lower with increasing frailty (14.00 [95% CI 13.75-14.19] for not frail vs 9.71 [95% CI 9.01-10.42] for severely frail patients; p < 0.05), but the change at 2 years was not significantly different among all groups (2.43 [95% CI 2.16-2.71] for not frail vs 2.56 [95% CI 1.10-4.02] for severely frail). The SF-36 delta values were also not different among groups, but significantly worse at baseline with increasing frailty. The odds ratio of achieving MCID for mJOA was significantly higher in the not frail group (1.89 [95% CI 1.36-2.61]; p < 0.05) compared to the other frailty cohorts, which remained after adjusting for age, approach, levels treated, and sex. The odds ratio of achieving MCID for the SF-36 domains was similar among all frailty groups. CONCLUSIONS: Increasing frailty is associated with worse baseline functional and quality of life measures in patients undergoing surgery for DCM. Frailty does not affect the magnitude of improvement in outcome measures after surgery, but reduces the chance of achieving the MCID for functional impairment significantly. Preoperative frailty assessment can therefore help guide clinicians in managing expectations after surgery for DCM. Potentially modifiable factors should be optimized in frail patients preoperatively to enhance functional outcomes.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.083
GPT teacher head0.334
Teacher spread0.250 · 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.

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

Citations12
Published2023
Admission routes1
Has abstractyes

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