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Record W7004881477

Outpatient rehabilitation for a patient with hip osteoarthritis

2018· other· en· W7004881477 on OpenAlexaboutno aff

Bibliographic record

VenueCSUN ScholarWorks (California State University, Northridge) · 2018
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsnot available
FundersCalifornia State University, Sacramento
KeywordsAmbulatoryOsteoarthritisPsychological interventionRehabilitationFunctional trainingSquatRange of motionAmbulatory care
DOInot available

Abstract

fetched live from OpenAlex

A patient with osteoarthritis of the hip was seen for nine physical therapy sessions at the California State University Sacramento pro-bono clinic.The sessions were provided by a physical therapist student under the supervision of a licensed physical therapist.During the patient's initial examination, the following measures were used to identify her body structure and function impairments, activity limitations, and participation restrictions: Numeric Pain Rating Scale, hand held dynamometry, Goniometry, The Western Ontario and McMaster Universities Osteoarthritis Index, Five Times Sit to Stand Test, and patient reports on participation.A plan of care was established.Goals for the patient were to decrease pain, improve hip range of motion and strength, functional mobility, ambulatory ability, and functional independence.Main interventions used were impairment based manual therapy, functional training, body weight supported treadmill training, and task-specific training.On discharge, the patient demonstrated improvements in pain, hip strength, functional mobility, ambulatory ability, and had progressed towards functional independence.She was able to achieve her personal goal of participating in her community walking program and to squat down to vi do gardening without pain.The patient was discharged from physical therapy with a home exercise program.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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