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SEVERE CERVICAL MYELOPATHY: APPROACHES AND POSTOPERATIVE EVALUATION

2021· article· en· W4205415646 on OpenAlexaff
Gabriel Faria Cerqueira, Álynson Larocca Kulcheski, André Luís Sebben, Pedro Grein Del Santoro, Marcel Luiz Benato, Xavier Soler i Graells

Bibliographic record

VenueColuna/Columna · 2021
Typearticle
Languageen
FieldMedicine
TopicCervical and Thoracic Myelopathy
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineMyelopathyOrthopedic surgeryVisual analogue scaleSurgeryRetrospective cohort studyObservational studySpinal cordInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Objectives: To evaluate and compare the clinical evolution of surgical approaches used in patients with severe cervical myelopathy. Methods: Retrospective observational study in which 19 patients with myelopathy who underwent surgery were evaluated. Neurological assessments using the Frankel scale were conducted both preoperatively and one year following surgery, and the modified Japanese Orthopedic Association (JOA), Nurick, and Visual Analog Scale for pain (VAS) questionnaires were applied 1 year after the surgical procedure. Results: 89% of the participants were male and the average age was 63.9 years. No patient had postoperative neurological worsening, 12 patients (63.16%) had mild pain, and seven (36.84%) had moderate pain. The group with degenerative disease showed neurological improvement after surgery and the exclusively anterior approach was used in 84% of the cases, the exclusively posterior approach in 10% of the cases, and the dual approach in 6% of the cases. Conclusion: Surgical treatment has good results for inhibiting the unfavorable natural evolution of myelopathy within 1 year following surgery and promotes neurological improvement in degenerative cases, making it possible to use the anterior access route in most cases. Level of evidence III; Retrospective Study.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.999

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)0.0020.000

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 head0.055
GPT teacher head0.292
Teacher spread0.237 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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