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Record W1741489887 · doi:10.3844/amjsp.2012.231.236

Assessment of Risk of Falls in Elderly Patients with Osteoarthritis of the Knee and Low Back Pain

2012· article· en· W1741489887 on OpenAlexaboutno aff
A.

Bibliographic record

VenueCurrent Research in Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACOsteoarthritisBerg Balance ScalePhysical therapyBalance (ability)MedicineSignificant differenceVisual analogue scaleLow back painTimed Up and Go testPhysical medicine and rehabilitationInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

This study aimed identify which groups of elderly are at higher risk of falls, with osteoarthritis (OA), low back pain or both conditions.29 elderly were distributed in three groups: OA of the Knee (OAK), Low Back Pain (LBP) and the two events concomitantly (OL).Evaluation tools applied were: Mini Mental State Examination (MMSE), Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Roland Morris Functional Performance Questionnaire, Gait Speed (GS), Timed Get Up and Go Test (TGUGT) and Berg Balance Scale (BBS).No differences were found in the three groups for assessment by GS, TGUGT and BBS instruments.A significative difference was found by RMQ in groups LPB and OL, WOMAC found difference in pain domain between groups OL and OAK and VAS analysis showed significative difference between group OL to the others groups.No significative difference was found between groups by the assessment of balance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.384
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2012
Admission routes1
Has abstractyes

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