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Record W4378550421 · doi:10.3171/2023.4.spine2382

Association of upper-limb neurological recovery with functional outcomes in high cervical spinal cord injury

2023· article· en· W4378550421 on OpenAlexaff
Saad Javeed, Jacob K. Greenberg, Justin K. Zhang, Benjamin A. Plog, Christopher F. Dibble, Braeden Benedict, Kathleen Botterbush, Jawad M. Khalifeh, Huacong Wen, Yuying Chen, Yikyung Park, Allan J. Belzberg, Sami Tuffaha, S. Shelby Burks, Allan D. Levi, Eric L. Zager, Amir H. Faraji, Mark A. Mahan, Rajiv Midha, Thomas J. Wilson, Neringa Juknis, Wilson Z. Ray

Bibliographic record

VenueJournal of Neurosurgery Spine · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSpinal cord injuryTetraplegiaUpper limbPhysical medicine and rehabilitationElbowPhysical therapyRehabilitationProspective cohort studyWristFunctional Independence MeasureOdds ratioSpinal cordSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: High cervical spinal cord injury (SCI) results in complete loss of upper-limb function, resulting in debilitating tetraplegia and permanent disability. Spontaneous motor recovery occurs to varying degrees in some patients, particularly in the 1st year postinjury. However, the impact of this upper-limb motor recovery on long-term functional outcomes remains unknown. The objective of this study was to characterize the impact of upper-limb motor recovery on the degree of long-term functional outcomes in order to inform priorities for research interventions that restore upper-limb function in patients with high cervical SCI. METHODS: A prospective cohort of high cervical SCI (C1-4) patients with American Spinal Injury Association Impairment Scale (AIS) grade A-D injury and enrolled in the Spinal Cord Injury Model Systems Database was included. Baseline neurological examinations and functional independence measures (FIMs) in feeding, bladder management, and transfers (bed/wheelchair/chair) were evaluated. Independence was defined as score ≥ 4 in each of the FIM domains at 1-year follow-up. At 1-year follow-up, functional independence was compared among patients who gained recovery (motor grade ≥ 3) in elbow flexors (C5), wrist extensors (C6), elbow extensors (C7), and finger flexors (C8). Multivariable logistic regression evaluated the impact of motor recovery on functional independence in feeding, bladder management, and transfers. RESULTS: Between 1992 and 2016, 405 high cervical SCI patients were included. At baseline, 97% of patients had impaired upper-limb function with total dependence in eating, bladder management, and transfers. At 1 year of follow-up, the largest proportion of patients who gained independence in eating, bladder management, and transfers had recovery in finger flexion (C8) and wrist extension (C6). Elbow flexion (C5) recovery had the lowest translation to functional independence. Patients who achieved elbow extension (C7) were able to transfer independently. On multivariable analysis, patients who gained elbow extension (C7) and finger flexion (C8) were 11 times more likely to gain functional independence (OR 11, 95% CI 2.8-47, p < 0.001) and patients who gained wrist extension (C6) were 7 times more likely to gain functional independence (OR 7.1, 95% CI 1.2-56, p = 0.04). Older age (≥ 60 years) and motor complete SCI (AIS grade A-B) reduced the likelihood of gaining independence. CONCLUSIONS: After high cervical SCI, patients who gained elbow extension (C7) and finger flexion (C8) had significantly greater independence in feeding, bladder management, and transfers than those with recovery in elbow flexion (C5) and wrist extension (C6). Recovery of elbow extension (C7) also increased the capability for independent transfers. This information can be used to set patient expectations and prioritize interventions that restore these upper-limb functions in patients with high cervical SCI.

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)0.002
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 score0.043
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.346
Teacher spread0.294 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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