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Record W7001260729

Intervención de la terapia ocupacional en rizartrosis: revisión bibliográfica

2019· article· en· W7001260729 on OpenAlexaboutno aff

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2019
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyPsychological interventionVisual analogue scaleThumbOsteoarthritisManual therapy
DOInot available

Abstract

fetched live from OpenAlex

Objective: This review aims to identify the effects of different occupational therapy interventions in patients suffering from osteoarthritis of first carpometacarpal joint, to know the types of treatments available for occupational therapists, and to establish various tests and standardized scales that can be applied in the assessment of this condition. Methods: A literature review was carried out using five databases. Articles related to our study, which had been published in the last ten years (June 2007- June 2017) in English or Spanish, were examined. An additional criterion was selecting samples with patients aged between 18 and 65. Results: A total of eight articles were assessed in which occupational therapy has proved to have beneficial effects on maintaining the dynamic stability of the trapeziometacarpal joint, using different techniques such as joint protection, orthotic treatment, treatment of fine motor skills and manual exercises with the aim to improve joint range and muscle strength. Conclusion: There are some discrepancies about which method of treatment is the most appropriate as well as a consensus regarding the application of these therapies. In addition, the assessment tools for this pathology have been tested, these being the Visual Analog Scale (VAS), the Disabilities of the Arm, Shoulder and Hand (DASH), the Australian Canadian Osteoarthritis Hand Index (AUSCAN), the Purdue Pegboard and the O’Connor test.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.293
Teacher spread0.284 · 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
Published2019
Admission routes1
Has abstractyes

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