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Record W4318918981 · doi:10.1097/brs.0000000000004552

Deterioration After Surgery for Degenerative Cervical Myelopathy: An Observational Study From the Canadian Spine Outcomes and Research Network

2022· article· en· W4318918981 on OpenAlexaffabout
Nathan Evaniew, Lukas D. Burger, Nicolas Dea, David W. Cadotte, Christopher S. Bailey, Sean Christie, Charles G. Fisher, Y. Raja Rampersaud, Jérôme Paquet, Supriya Singh, Michael H. Weber, Najmedden Attabib, Michael G. Johnson, Neil Manson, Philippe Phan, Andrew Nataraj, Jefferson R. Wilson, Hamilton Hall, Greg McIntosh, W. Bradley Jacobs

Bibliographic record

VenueSpine · 2022
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsUniversity of AlbertaCanada East Spine CentreWestern UniversityUniversity of TorontoDalhousie UniversityUniversity of OttawaMcGill UniversityAlberta Bone and Joint Health InstituteUniversity of British ColumbiaUniversity of ManitobaCentre hospitalier universitaire de QuébecFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineMyelopathyIncidence (geometry)CohortProspective cohort studyAdverse effectCohort studyEtiologyQuality of life (healthcare)SurgeryInternal medicineSpinal cord

Abstract

fetched live from OpenAlex

STUDY DESIGN: A Prospective cohort study. OBJECTIVE: To investigate the incidence, etiology, and outcomes of patients who experience neurological deterioration after surgery for Degenerative Cervical Myelopathy (DCM). SUMMARY OF BACKGROUND DATA: Postoperative neurological deterioration is one of the most undesirable complications that can occur after surgery for DCM. METHODS: We analyzed data from the Canadian Spine Outcomes and Research Network DCM prospective cohort study. We defined postoperative neurological deterioration as any decrease in modified Japanese Orthopaedic Association (mJOA) score by at least one point from baseline to three months after surgery. Adverse events were collected using the Spinal Adverse Events Severity protocol. Secondary outcomes included patient-reported pain, disability, and health-related quality of life. RESULTS: Among a study cohort of 428 patients, 50 (12%) deteriorated by at least one mJOA point after surgery for DCM (21 by one point, 15 by two points, and 14 by three points or more). Significant risk factors included older age, female sex, and milder disease. Among those who deteriorated, 13 experienced contributing intraoperative or postoperative adverse events, six had alternative non-DCM diagnoses, and 31 did not have an identifiable reason for deterioration. Patients who deteriorated had significantly lower mJOA scores at one year after surgery [13.5 (SD 2.7) vs. 15.2 (SD 2.2), P <0.01 and those with larger deteriorations were less likely to recover their mJOA to at least their preoperative baseline, but most secondary measures of pain, disability, and health-related quality of life were unaffected. CONCLUSIONS: The incidence of deterioration of mJOA scores after surgery for DCM was approximately one in 10, but some deteriorations were unrelated to actual spinal cord impairment and most secondary outcomes were unaffected. These findings can inform patient and surgeon expectations during shared decision-making, and they demonstrate that the interpretation of mJOA scores without clinical context can sometimes be misleading.

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)0.002
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 score0.219
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.211
GPT teacher head0.399
Teacher spread0.188 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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