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Record W4390694691 · doi:10.1227/neu.0000000000002818

Elderly Patients Show Substantial Improvement in Health-Related Quality of Life After Surgery for Degenerative Cervical Myelopathy Despite Medical Frailty: An Ambispective Analysis of a Multicenter, International Data Set

2024· article· en· W4390694691 on OpenAlexaff
Karlo M. Pedro, Mohammed Ali Alvi, Nader Hejrati, Ali Moghaddamjou, Michael G. Fehlings

Bibliographic record

VenueNeurosurgery · 2024
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsToronto Western HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineMyelopathyQuality of life (healthcare)Frailty IndexDegenerative diseaseMulticenter studySurgeryPhysical therapyGerontologyCentral nervous system diseaseSpinal cordRandomized controlled trialNursing

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: We assessed the relationship between Modified Frailty Index-5 (mFI-5) and neurological outcomes, as well as health-related quality of life (HRQoL) measures, in elderly patients with degenerative cervical myelopathy (DCM) after surgery. METHODS: Data from 3 major DCM trials (the Arbeitsgemeinschaft für Osteosynthesefragen Spine Cervical Spondylotic Myelopathy-North America, Cervical Spondylotic Myelopathy-International, and CSM-PROTECT studies) were combined, involving 1047 subjects with moderate to severe myelopathy. Patients older than 60 years with 6-month and 1-year postoperative data were analyzed. Neurological outcome was assessed using the modified Japanese Orthopaedic Association score, while HRQoL was measured using the 36-Item Short Form Health Survey (SF-36) (both Physical Component Summary [SF-36 PCS] and Mental Component Summary [SF-36 MCS] scores) and the Neck Disability Index. Frail (mFI ≥2) and nonfrail (mFI = 0-1) cohorts were compared using univariate paired statistics. RESULTS: The final analysis included 261 patients (62.5% male), with a mean age of 71 years (95% CI 70.7-72). Frail patients (mFI ≥2) had lower baseline modified Japanese Orthopaedic Association scores (10.45 vs 11.96, P < .001), SF-36 PCS scores (32.01 vs 36.51, P < .001), and SF-36 MCS scores (39.32 vs 45.24, P < .001). At 6-month follow-up, SF-36 MCS improved by a mean (SD) of 7.19 (12.89) points in frail vs 2.91 (11.11) points in the nonfrail group ( P = .016). At 1 year after surgery, frail patients showed greater improvement in both SF-36 PCS and SF-36 MCS composite scores compared with nonfrail patients (7.81 vs 4.49, P = .038, and 7.93 vs 3.01, P = .007, respectively). Bivariate regression analysis revealed that higher mFI-5 scores correlated with more substantial improvement in overall mental status at 6 months and 1 year ( P = .024 and P = .009, respectively). CONCLUSION: mFI-5 is a clinically helpful signature to reflect the HRQoL status among elderly patients with DCM. Despite preoperative medical frailty, elderly patients with DCM experience significant HRQoL improvement after surgery. These findings enable clinicians to identify elderly patients with modifiable comorbidities and provide informed counseling on anticipated outcomes. LEVEL OF EVIDENCE: II.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.083
GPT teacher head0.370
Teacher spread0.287 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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