MétaCan
Menu
Back to cohort

The Role of Physical Therapy in Improving Balance and Fall Prevention in the Elderly

2024· article· en· W4405657772 on OpenAlexaff
‏Abduallah Saleh S. Almutiri, Hamad Nasser Mubark Alshadied, Asma ali altalhi, Abdullah M. AlShahrani, Ohoud Ebraheem ALREFAI, Yuosef Abdulrahman Abdullah Alkafari, Hassan Mohammed almuashi, Mohammed M. Alyami, Salma yahya darraj, Abdulaziz Ibrahim Bahni Almathre, Abdullah Raad Abdullah Alruaydan, Abdullah saleh Alonazi, Abdulrahman Mohammed Aseeri, Salma Alshehri

Bibliographic record

VenueEgyptian Journal of Chemistry · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsMinistry of Health and Long Term Care
Fundersnot available
KeywordsBalance (ability)Fall preventionGerontologyPsychologyMedicinePhysical therapySuicide preventionPoison controlEnvironmental health

Abstract

fetched live from OpenAlex

Background: The two leading problems of elderly people are balance disorders and falls, which make them become incapacitated due to critical conditions. Physical therapy is considered as one of the methods which are helpful for such problems, but comprehensive treatment should include efforts of several specialists.Aim: The purpose of this study is to examine the feasibility of integrated care working models applied to elder care specially in the physical therapy realm to address issues of balance and threat of falling.Methods: Systematic mediated literature review regarding interdisciplinary teamwork in elderly care and balance and fall prevention.Results: Role integration with the physical therapist, the doctor, the nurse and others optimizes a patient’s balance, concerns related to falling and patient outcomes through teamwork and individual care plans.Conclusion: The integration of members of different areas of specialization is crucial in handling elderly patients especially because physical therapy ensures proper coordination of movements, improved care and finally a very minimal fall risk.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.406
Teacher spread0.387 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEgyptian Journal of ChemistrySame topicHealth and Wellbeing ResearchFrench-language works237,207