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Record W4313473660 · doi:10.30621/jbachs.1155794

Evaluation of Force Sense, Functional Performance, Quality of Life, Activity Level and Kinesiophobia in Degenerative Meniscal Tears Following Partial Meniscectomy

2023· article· en· W4313473660 on OpenAlexaboutno aff
Cansu Gevrek Aslan, Ahmet Özgür Atay, Gizem İrem Kınıklı

Bibliographic record

VenueJournal of Basic and Clinical Health Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
FundersHacettepe Üniversitesi
KeywordsTearsMedicineQuality of life (healthcare)Physical medicine and rehabilitationPhysical therapySurgery

Abstract

fetched live from OpenAlex

Objective: The aim of this study was to compare functional performance, force sense of knee joint, kinesiophobia, quality of life, and activity level between patients with partial meniscectomy and healthy people. Methods: Twenty patients with partial meniscectomy within six months to three years postoperatively and 20 healthy were included in this study. Maximal voluntary isometric muscle strength of Quadriceps femoris muscle and force sense with the biofeedback device, quality of life with Western Ontario Meniscal Evaluation Tool, functional performance with stair up/down test, physical function with Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form, activity level with Tegner Activity Level scale, kinesiophobia was evaluated with Brief Fear of Movement Scale. Results: Range of motion, maximal voluntary isometric muscle strength of M. Quadriceps femoris, and force sense decreased in the operated leg compared with the non-operated leg (p

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)0.001
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
Metaresearch0.0000.001
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)0.0010.000

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 head0.281
GPT teacher head0.495
Teacher spread0.214 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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