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Record W4403459105 · doi:10.15453/2168-6408.2237

Occupational Therapist-Led Remedial Vision Program after Mild Traumatic Brain Injury: Pre/Post Pilot Study

2024· article· en· W4403459105 on OpenAlexaboutno aff
Suzanne Briggs, Mitchell Scheiman, Yuki Asakura

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyRemedial educationTraumatic brain injuryPsychologyPhysical medicine and rehabilitationMedicinePhysical therapyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Background: Vision disorders are common after mild traumatic brain injury (mTBI) and can affect occupational performance. The study was designed to support occupational therapy’s role in providing remedial vision rehabilitation (RVR) by demonstrating changes in vision efficiency and patient reports of vision-related occupational performance in adult patients with a mTBI after occupational therapy-led RVR. Method: In this retrospective study, data was collected pre/post-RVR treatment at an outpatient clinic using a convenience sample of adults 18 years of age and older with vision disorder diagnosis and mTBI diagnosis. Vertical/horizontal saccades, vergence jumps, near-point convergence, Convergence Insufficiency Symptom Survey (CISS), and Canadian Occupational Performance Measurement (COPM) were measured before and after RVR. Results: Statistically significant changes were found in all outcome measurement scores after RVR with a large-size effect using t-test analysis. (Vertical saccades: t = -2.71; p = .022. Horizontal saccades t = -3.87; p = .003. Vergence jumps t =- 4.98; p= .001 Conclusions: Evidence-based RVR after mTBI injury may improve vision efficiency disorders and vision-related occupational performance and satisfaction, demonstrating occupational therapy’s distinct role.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.242
GPT teacher head0.507
Teacher spread0.265 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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