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Record W4295979638 · doi:10.3171/2022.7.spine22592

Prediction of 2-year clinical outcome trajectories in patients undergoing anterior cervical discectomy and fusion for spondylotic radiculopathy

2022· article· en· W4295979638 on OpenAlexafffund
Jeffrey J. Hébert, Tyler Adams, Erin Cunningham, Dana El-Mughayyar, Neil Manson, Edward Abraham, Niels Wedderkopp, Erin Bigney, Eden Richardson, Amanda Vandewint, Chris Small, George Kolyvas, André le Roux, Aaron Robichaud, Michael H. Weber, Charles G. Fisher, Nicolas Dea, Stephan du Plessis, Raphaële Charest-Morin, Sean Christie, Christopher S. Bailey, Y. Raja Rampersaud, Michael G. Johnson, Jérôme Paquet, Andrew Nataraj, Bernard LaRue, Hamilton Hall, Najmedden Attabib

Bibliographic record

VenueJournal of Neurosurgery Spine · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsUniversité LavalUniversity of Alberta HospitalUniversity Health NetworkHorizon Health NetworkLondon Health Sciences CentreUniversité de SherbrookeUniversity of TorontoWestern UniversityDartmouth General HospitalUniversity of CalgaryMcGill UniversityCanada East Spine CentreUniversity of ManitobaMontreal General HospitalUniversity of New BrunswickDalhousie UniversityAlberta Hospital EdmontonUniversity of British Columbia
FundersCanadian Institutes of Health ResearchFondation de la recherche en santé du Nouveau-BrunswickStrykerNuVasive
KeywordsMedicineAnterior cervical discectomy and fusionSurgeryOutcome (game theory)FusionCervical spineMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: Anterior cervical discectomy and fusion (ACDF) is often described as the gold standard surgical technique for cervical spondylotic radiculopathy. Although outcomes are considered favorable, there is little prognostic evidence to guide patient selection for ACDF. This study aimed to 1) describe the 24-month postoperative trajectories of arm pain, neck pain, and pain-related disability; and 2) identify perioperative prognostic factors that predict trajectories representing poor clinical outcomes. METHODS: In this retrospective cohort study, patients with cervical spondylotic radiculopathy who underwent ACDF at 1 of 12 orthopedic or neurological surgery centers were recruited. Potential outcome predictors included demographic, health, clinical, and surgery-related prognostic factors. Surgical outcomes were classified by trajectories of arm pain intensity, neck pain intensity (numeric pain rating scales), and pain-related disability (Neck Disability Index) from before surgery to 24 months postsurgery. Trajectories of postoperative pain and disability were estimated with latent class growth analysis, and prognostic factors associated with poor outcome trajectory were identified with robust Poisson models. RESULTS: The authors included data from 352 patients (mean age 50.9 [SD 9.5] years; 43.8% female). The models estimated that 15.5%-23.5% of patients followed a trajectory consistent with a poor clinical outcome. Lower physical and mental health-related quality of life, moderate to severe risk of depression, and longer surgical wait time and procedure time predicted poor postoperative trajectories for all outcomes. Receiving compensation and smoking additionally predicted a poor neck pain outcome. Regular exercise, physiotherapy, and spinal injections before surgery were associated with a lower risk of poor disability outcome. Patients who used daily opioids, those with worse general health, or those who reported predominant neck pain or a history of depression were at greater risk of poor disability outcome. CONCLUSIONS: Patients who undergo ACDF for cervical spondylotic radiculopathy experience heterogeneous postoperative trajectories of pain and disability, with 15.5%-23.5% of patients experiencing poor outcomes. Demographic, health, clinical, and surgery-related prognostic factors can predict ACDF outcomes. This information may further assist surgeons with patient selection and with setting realistic expectations. Future studies are needed to replicate and validate these findings prior to confident clinical implementation.

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)0.001
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 score0.010
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.041
GPT teacher head0.306
Teacher spread0.265 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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