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Record W2462216641

[Early clinical outcomes of fixed-bearing versus mobile-bearing total knee arthroplasty].

2011· article· en· W2462216641 on OpenAlexaboutno aff
Yun Zeng, Li Cao, Liu Yang, Gao-feng Peng, Li-bin Peng, Desheng Yang, A.M. Deli, Boyong Xu, Baojun Gong

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACMedicineProsthesisOsteoarthritisTotal knee arthroplastyRange of motionArthroplastyRandomized controlled trialClinical trialPhysical therapySurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the early clinical outcomes of primary total knee arthroplasty by a fixed-bearing versus mobile-bearing prosthesis. METHODS: A total of 80 patients with osteoarthritis at our hospital from January 2008 to October 2008 were sequentially and randomly assigned into Group A (fixed-bearing prosthesis) (40 knees) and Group B (mobile-bearing prosthesis) (40 knees). And the data of the range of motion (ROM), Knee Society Score (KSS) and Western Ontario MacMaster (WOMAC) were collected at pre-operation and 6, 12 and 24 months post-operation respectively. RESULTS: The P values were as follows: KSS: 0.754, 0.802, 0.561, 0.764; HSS (Hospital for Special Surgery): 0.737, 0.361, 0.254, 0.330; WOMAC (Western Ontario and McMaster Universities) osteoarthritis index: 0.976, 0.557, 0.946, 0.818; ROM follow-up: 0.519, 0.646, 0.175, 0.276. No significant differences were found in clinical outcomes between two groups. CONCLUSION: The fixed-bearing and mobile-bearing prostheses show no difference in clinical outcomes.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0030.000

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 head0.064
GPT teacher head0.289
Teacher spread0.225 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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