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Record W2335089984 · doi:10.1097/brs.0000000000000678

Role of Magnetic Resonance Imaging in Predicting Surgical Outcome in Patients With Cervical Spondylotic Myelopathy

2014· article· en· W2335089984 on OpenAlexaff
Aria Nouri, Lindsay Tetreault, Juan J. Zamorano, Kristian Dalzell, Aileen M. Davis, David J. Mikulis, Albert Yee, Michael G. Fehlings

Bibliographic record

VenueSpine · 2014
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto Western Hospital
Fundersnot available
KeywordsMedicineMagnetic resonance imagingConfidence intervalOdds ratioMyelopathyRetrospective cohort studyOrthopedic surgeryUnivariate analysisNuclear medicineHyperintensityRadiologyMultivariate analysisSurgeryInternal medicineSpinal cord

Abstract

fetched live from OpenAlex

In Brief Study Design. Ambispective, retrospective cohort study from prospectively collected data. Summary of Background Data. Cervical spondylotic myelopathy is the commonest cause of spinal cord impairment in the elderly population worldwide. Although magnetic resonance imaging (MRI) is the primary imaging modality for confirming the diagnosis, its role in predicting surgical outcome remains unclear. Methods. Two hundred seventy-eight patients with 1 or more clinical signs of myelopathy were enrolled; and they underwent decompression surgery. Complete baseline clinical and MRI data were available for 102 patients. MRI parameters measured included presence/absence of signal change on T1 and T2, T2 signal quantitative factors, and anatomical measurements. A dichotomized postoperative modified Japanese Orthopedic Association (mJOA) score at 6 months was used to characterize patients with mild myelopathy (≥16) and those with substantial residual neurological impairment (<16). Univariate analysis assessed the relationship between baseline parameters and outcome. Multivariate logistic regression was conducted after a conceptual division of variables into 3 groups: T1 signal analysis, T2 signal analysis, and anatomical measurements. Results. Baseline mJOA (P < 0.001; odds ratio [OR] = 1.644, 95% confidence interval [95% CI]: 1.326–2.037), maximum canal compromise (MCC) (P = 0.0322; OR = 0.965, 95% CI: 0.934–0.997), T2 hyperintensity region of interest area (P = 0.0422; OR = 0.67; 95% CI: 0.456–0.986), and sagittal extent (P = 0.026; OR = 0.673; 95% CI: 0.475–0.954) were significantly associated with outcome univariately. The final model was comprised of T1 hypointensity (P = 0.029; OR = 0.242; CI: 0.068–0.866), MCC (P = 0.005; OR = 0.940; CI: 0.90–0.982) and baseline mJOA (P < 0.001; OR = 1.743; CI: 1.353–2.245), yielding an area under the receiver operating characteristic curve (AUC) of 0.845. Conclusion. Baseline mJOA is a strong predictor of postsurgical outcome in cervical spondylotic myelopathy at 6 months. However, a model inclusive of MCC and T1 hypointensity assessment provides superior predictive capacity. This suggests that MRI analysis has a significant role in predicting surgical outcome. Level of Evidence: 3 The role of magnetic resonance imaging (MRI) in predicting surgical outcome in cervical spondylotic myelopathy remains unclear. MRI data for patients who underwent decompression surgery were examined. Findings indicate that baseline modified Japanese Orthopedic Association is a strong predictor of postsurgical outcome and that a model including maximum canal compromise and T1 hypointensity assessment provides superior predictive capacity.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.145
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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.

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

Citations112
Published2014
Admission routes1
Has abstractyes

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