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Record W2227550344 · doi:10.2106/jbjs.o.00189

A Clinical Prediction Rule for Functional Outcomes in Patients Undergoing Surgery for Degenerative Cervical Myelopathy

2015· article· en· W2227550344 on OpenAlexaff
Lindsay Tetreault, Branko Kopjar, Pierre Côté, Paul M. Arnold, Michael G. Fehlings

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsOntario Tech UniversityCentre for Disability Prevention and RehabilitationToronto East General HospitalToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineMyelopathyClinical prediction ruleConfidence intervalUnivariateUnivariate analysisPoisson regressionInternal medicineRelative riskSurgeryMultivariate analysisPhysical therapyMultivariate statisticsSpinal cordPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Cervical spondylotic myelopathy (CSM) is a progressive spinal condition that is often managed surgically. Knowledge of important predictors of surgical outcome can provide decision support to surgeons and enable them to effectively manage their patients' expectations. The purpose of this study was to identify the most important clinical predictors of surgical outcome in patients with CSM using data from two multinational prospective studies. METHODS: A total of 757 patients treated surgically for CSM participated in either the CSM-North America or the CSM-International study. The model was designed to distinguish between patients who achieved a modified Japanese Orthopaedic Association (mJOA) score of ≥16 at the one-year follow-up and those who did not (mJOA < 16). A score of 16 was chosen as the cutoff as an mJOA of ≥16 translates to minimal impairment. Univariate analyses evaluated the relationship between outcome and various clinical predictors. Multivariate Poisson regression was used to create the final prediction rule and estimate relative risks. RESULTS: Based on univariate analyses, the probability of achieving a score of ≥16 decreased with the presence of certain symptoms, including gait dysfunction, the presence of certain signs such as lower limb spasticity, positive smoking status, higher comorbidity score, more severe preoperative myelopathy, and older age. The final model consisted of six significant and clinically relevant predictors: baseline severity score (relative risk [RR], 1.11; 95% confidence interval [CI], 1.07 to 1.15), impaired gait (RR, 0.76 [ref. = absence]; 95% CI, 0.66 to 0.88), age (RR, 0.91 per decade; 95% CI, 0.85 to 0.96), comorbidity score (RR, 0.93; 95% CI, 0.88 to 0.98), smoking status (RR, 0.78 [ref. = non-smoking]; 95% CI, 0.65 to 0.93), and duration of symptoms (RR, 0.95; 95% CI, 0.90 to 0.99). CONCLUSIONS: Patients were more likely to achieve a score of ≥16 (indicating minimal impairment) if they were younger, had milder preoperative myelopathy, did not smoke, had fewer and less severe comorbidities, did not present with impaired gait, and had shorter symptom duration.

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)0.012
metaresearch head score (Gemma)0.043
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 score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.095
GPT teacher head0.314
Teacher spread0.218 · 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

Citations136
Published2015
Admission routes1
Has abstractyes

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