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Osteopathic Correction in Complex Therapy of Patients with Mild Cognitive Impairment

2017· article· en· W2946007953 on OpenAlexaboutno aff
O. F. Anufriev, V. O. Belash, V. M. Utochkin

Bibliographic record

VenueRussian Osteopathic Journal · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCognitive impairmentAnxietyNeuropsychologyPhysical therapyCognitionDepression (economics)Clinical psychologyPediatricsPsychiatry

Abstract

fetched live from OpenAlex

Goal of research - the study aims to evaluate the effectiveness and scientifi c basis for the use of osteopathic correction in the treatment of cognitive impairment.Materials and methods. Two groups of patients participated in the study: Group 1 (focus group) included 22 patients with a syndrome of mild cognitive impairment who received medication and osteopathic correction; Group 2 (control group) consisted of 22 patients who received medication only. The groups were comparable in age, sex, and severity of cognitive impairment. All patients observed before and after treatment underwent complex osteopathic and neuropsychological examinations.Results. The study found that the inclusion of osteopathic correction in the treatment of patients with mild cognitive impairment has a positive effect on the dynamics of a number of neuropsychological tests. In particular, there is a statistically signifi cant increase in the indicators based on the results of the Montreal Cognitive Evaluation Scale; there is a decrease in the overall anxiety level based on the results of the Hospital Alarm and Depression Scale. Moreover, the focus group shows a statistically signifi cant decrease in the severity of somatic dysfunction in the head and neck area, both structural and visceral components, which was not observed in the control group.Conclusion. The results from the study suggest recommending the inclusion of osteopathic correction in the complex therapy for patients with mild cognitive impairment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.628

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 studies0.0010.000
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 head0.039
GPT teacher head0.280
Teacher spread0.241 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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