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Record W3043398492

[PECULIARITIES OF LEGAL SUPPORT MEDICAL REHABILITATION OR HABILITATION OF PERSONS WITH DISABILITIES ELDERLY AND SENILE ON THE EXAMPLE].

2020· article· en· W3043398492 on OpenAlexaboutno aff
N. Yu. Stasevich, N. E. Zlatkina, D Starzev, Slava Kozlov

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsHabilitationRehabilitationMedicinePopulationSpecialtyQuarter (Canadian coin)Medical careGerontologyPhysical therapyFamily medicineEnvironmental healthHumanities
DOInot available

Abstract

fetched live from OpenAlex

According to the world Health Organization, more than a quarter of all patients receiving inpatient treatment and up to half of all outpatient patients need rehabilitation treatment. In Russia, the need of the elderly and senile population for medical rehabilitation in multi-specialty health care institutions is up to 400 people per 1000 population. The research was conducted on the basis of a systematic approach and the use of modern analytical and statistical methods. The purpose of the study: to analyze the features of regulatory support for medical rehabilitation and habilitation of elderly and senile persons at the present stage.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
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 integrity0.0000.000
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 head0.068
GPT teacher head0.297
Teacher spread0.229 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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