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Record W3169459933 · doi:10.1097/phm.0000000000001812

Early Clinical Prediction of Independent Outdoor Functional Walking Capacity in a Prospective Cohort of Traumatic Spinal Cord Injury Patients

2021· article· en· W3169459933 on OpenAlexaff
Stéphanie Jean, Jean‐Marc Mac‐Thiong, Marie-Christine Jean, Antoine Dionne, Jean Bégin, Andréane Richard‐Denis

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2021
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsMedicineSpinal cord injuryDermatomePhysical medicine and rehabilitationProspective cohort studySensationPhysical therapySpinal cordCohortTraumatic brain injuryAnesthesiaSurgeryPsychologyInternal medicineNeuroscience

Abstract

fetched live from OpenAlex

OBJECTIVE: The first objective was to identify a method for early prediction of independent outdoor functional walking 1 yr after a traumatic spinal cord injury using the motor and sensory function derived from the International Standards for Neurological Classification of Spinal Cord Injury assessment during acute care. Then, the second objective was to develop a clinically relevant prediction rule that would be accurate, easy to use, and quickly calculated in clinical setting. DESIGN: A prospective cohort of 159 traumatic spinal cord injury patients was analyzed. Bivariate correlations were used to determine the assessment method of motor strength and sensory function as well as the specific dermatomes and myotomes best associated with independent outdoor functional walking 1 yr after injury. An easy-to-use clinical prediction rule was produced using a multivariable linear regression model. RESULTS: The highest motor strength for a given myotome (L3 and L5) and preserved light touch sensation (dermatome S1) were the best predictors of the outcome. The proposed prediction rule displayed a sensitivity of 84.21%, a specificity of 85.54%, and a global accuracy of 84.91% for classification. CONCLUSIONS: After an acute traumatic spinal cord injury, accurately predicting the ability to walk is challenging. The proposed clinical prediction rule aims to enhance previous work by identifying traumatic spinal cord injury patients who will reach a mobility level that fosters social participation and quality of life in the chronic period after the injury. TO CLAIM CME CREDITS: Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME. CME OBJECTIVES: Upon completion of this article, the reader should be able to: (1) Revise the different motor and sensory function assessment methods used for prognostication of walking after an acute traumatic spinal cord injury; (2) Identify clinical factors that are significantly associated with functional walking 1 yr after a traumatic spinal cord injury; and (3) Accurately estimate the likelihood of reaching independent outdoor functional walking in the chronic phase after an acute traumatic spinal cord injury. LEVEL: Advanced. ACCREDITATION: The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.047
GPT teacher head0.388
Teacher spread0.341 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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