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Record W2734077806 · doi:10.15453/2168-6408.1383

Effectiveness of a Multidisciplinary Rehabilitation Program Following Shoulder Injury

2017· article· en· W2734077806 on OpenAlexaff
Andrea Bean, Cathryn Edmonds, Tukata Lin, Rachel Davis, Lisa Hopcroft, Alicia Savona, Gargi Singh, Kristina Boccia, Kyle Leming, Helen Mann, Helen Razmjou

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsRehabilitationMultidisciplinary approachOccupational therapyPhysical therapyMedicinePhysical medicine and rehabilitationOccupational safety and healthHealth care

Abstract

fetched live from OpenAlex

Background: Shoulder injuries in working age adults result in a major cost to the health care system. The purpose of this study was to examine the effectiveness of a new multidisciplinary rehabilitation program and to explore factors that affected a successful return to work (RTW) in injured workers with shoulder problems who received this program. Methods: This was a prospective longitudinal study. The patient-oriented outcome measures were the Numeric Pain Rating Scale (NPRS) and the Disabilities of the Arm, Shoulder, and Hand (DASH). Range of motion (ROM) in flexion, abduction, and external rotation and strength in lifting and push/pull were documented. All outcomes were measured before and at the completion of the program. Results: Data of 68 patients were used for analysis. All outcomes showed a statistically significant improvement over time. Conclusions: Multidisciplinary rehabilitation programs help to improve pain, disability, ROM, strength, and facilitate RTW. Higher stress and a fast-paced work environment increased the risk of not progressing in work status.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.001
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 head0.102
GPT teacher head0.487
Teacher spread0.385 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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