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Record W2890667301 · doi:10.1080/10790268.2018.1517138

Determining priorities in functional rehabilitation related to quality of life one-year following a traumatic spinal cord injury

2018· article· en· W2890667301 on OpenAlexaff
Andréane Richard‐Denis, Delphine Benazet, Cynthia Thompson, Jean‐Marc Mac‐Thiong

Bibliographic record

VenueJournal of Spinal Cord Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersU.S. Department of Defense
KeywordsMedicineSpinal cord injuryRehabilitationQuality of life (healthcare)Context (archaeology)Physical therapyConfoundingPsychological interventionTrauma centerCohort studyActivities of daily livingPhysical medicine and rehabilitationCohortRetrospective cohort studySpinal cordPsychiatrySurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Context/Objective: To determine the relationship between the different functional aspects (as determined by the Spinal Cord Independence Measure) and quality of life (QOL) following a traumatic spinal cord injury (TSCI), considering clinical confounding factors.Design: Retrospective review of a prospective cohortSetting: A single Level-1 trauma center specialized in SCI careParticipants: One hundred and forty-two individuals sustaining an acute traumatic SCIInterventions: Not applicableOutcome measures: The four QOL domains as assessed by the WHOQoL-bref questionnaire 6–12 months following a TSCI.Results: Mobility subscore was the only functional aspect significantly associated with all QOL domains (physical, psychological, social and environmental). Females present better chronic social and environmental QOL when compared to males. The level of injury may also influence environmental QOL.Conclusion: Mobility training (mobility in bed, mobility with or without technical aids, transfers and stair management) should be an important part of the rehabilitation process in order to optimize chronic QOL following a TSCI.

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)0.005
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
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 head0.144
GPT teacher head0.465
Teacher spread0.321 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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