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Record W2214688076 · doi:10.1310/sci1301-58

Enhancing Upper Extremity Function with Reconstructive Surgery in Persons with Tetraplegia: A Review of the Literature

2007· review· en· W2214688076 on OpenAlexafffund
Connolly Connolly, Aubut Aubut, Teasell Teasell, Tal Jarus

Bibliographic record

VenueTopics in Spinal Cord Injury Rehabilitation · 2007
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British ColumbiaParkwood InstituteWestern UniversityLawson Health Research InstituteSt Joseph's Health Care
FundersOntario Neurotrauma Foundation
KeywordsTetraplegiaMedicineReconstructive surgerySpinal cord injuryPhysical medicine and rehabilitationQuality of life (healthcare)Physical therapyPsychological interventionRehabilitationEvidence-based medicinePopulationSurgerySpinal cordNursingAlternative medicine

Abstract

fetched live from OpenAlex

Purpose: To review the evidence for interventions in peer-reviewed published literature for reconstructive surgical procedures in the upper extremity for persons with tetraplegia following spinal cord injury (SCI). Method: A critical review and synthesis of articles addressing reconstructive surgical procedures was conducted. Each article was assessed for quality using the Downs and Black evaluation tool. Following this, a level of evidence using a modified Sackett scale was assigned to each intervention within these categories. Results: Each of the surgical procedures reviewed had numerous pre-post or case series studies that provide level 4 evidence (grade C) to support the use of reconstructive surgical procedures in improving upper extremity motor and functional use of the limb in individuals with tetraplegia following an SCI. Conclusion: There is evidence that reconstructive surgical procedures enhance upper extremity function for persons with tetraplegia. Although there is a need for higher quality evidence through improved study designs, there are both ethical and logistical constraints that present challenges in studying this population.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.055
GPT teacher head0.399
Teacher spread0.344 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations23
Published2007
Admission routes2
Has abstractyes

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