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Record W2571944012 · doi:10.1055/s-0035-1554384

Role of Quantitative MRI Assessments in Predicting Surgical Outcome in Cervical Spondylotic Myelopathy Patients: Results from the Prospective, Multicenter AOSpine North American Study

2015· article· en· W2571944012 on OpenAlexaff
Michael G. Fehlings, Aria Nouri, Lindsay Tetreault, Kristian Dalzell, Juan J. Zamorano

Bibliographic record

VenueGlobal Spine Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineMyelopathyMagnetic resonance imagingOrthopedic surgeryLogistic regressionSpinal cord compressionRadiologyNuclear medicineSurgerySpinal cordInternal medicine

Abstract

fetched live from OpenAlex

Introduction Cervical spondylotic myelopathy (CSM) is the commonest cause of spinal cord impairment in the elderly population worldwide. Though recent efforts have uncovered valuable clinical predictors of outcome in patients undergoing surgical decompression, the utility of MRI assessment in this regard remains equivocal. To address this fundamental knowledge gap, it is the objective of this study to quantitatively assess the role of MRI in predicting surgical outcome using multicenter prospective data. Material and Methods A total of 278 patients with at least one clinical sign of CSM were enrolled in AOSpine North American Study. Of these, baseline MRI data and modified Japanese Orthopedic Association score (mJOA) assessment at 6 months were available for 101 patients. MRIs were reviewed by three investigators for the location of pathology and for presence or absence ( ± ) of signal change on T1 and T2 imaging. Quantitative analysis of T2 hyperintensity area, sagittal extent, and signal change ratios was also conducted. In addition, spinal canal compromise and spinal cord compression were measured on T2 imaging. The mJOA score was used as the primary outcome measure and was dichotomized to discriminate between patients with mild myelopathy postoperatively (≥ 16) and those with substantial residual neurological impairment (< 16). Univariate analyses assessed the relationship of baseline mJOA and MRI analysis with outcome. Logistic regression modeling followed a conceptual division of variables into three key groups: T1 signal analysis, T2 signal analysis, and anatomical measurements. Inclusion of variables in the final model was based on practical, clinical, and statistical considerations (including Akaike information criterion, AIC; Bayesian information criterion, BIC; area under the receiver operator curve characteristics; AUC). The final model was compared with a model containing only baseline mJOA using a likelihood-ratio test. Results In univariate analysis, baseline mJOA ( p < 0.0001), spinal canal compromise ( p = 0.0322), T2 hyperintensity area ( p = 0.0422), and maximum height ( p = 0.026) were all significantly associated with outcome. A single variable was used to describe T1 hypointensity ( ± ) and anatomical measurements (spinal canal compromise), and two variables were used to describe T2 hyperintensity signal characteristics (maximum height and Wang signal ratio) in the initial model. These four imaging variables along with baseline mJOA yielded an AUC of 0.849. Reduction of variables to create parsimony resulted in a final model including T1 hypointensity (OR = 0.242; CI: 0.068–0.866), spinal canal compromise (OR = 0.940; CI: 0.90–0.982), and baseline mJOA (OR = 1.743; CI: 1.353–2.245) with an AUC of 0.845, while reducing both the AIC and BIC. The AUC for the baseline mJOA-only model was 0.807. The likelihood-ratio test indicated superior performance of the full model compared with the mJOA-only model ( p < 0.0001). Conclusion Baseline mJOA is a strong predictor of postsurgical outcome in CSM at 6 months; however, a model inclusive of spinal canal compromise and T1 hypointensity assessment in addition to this provides a superior predictive capacity. This suggests that MRI analysis has a significant role in predicting surgical outcome. It is, therefore, recommended that a thorough MRI analysis be conducted in all patients with CSM considered for surgical treatment.

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)0.006
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 score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.026
GPT teacher head0.348
Teacher spread0.322 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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