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Preliminary clinical effects of total knee arthroplasty with iASSIST navigation system

2018· article· en· W3030763801 on OpenAlexaboutno aff
Desheng Wu, Zhonghao Deng, Yufan Chen, Zheting Liao, Shuhao Feng, Liang Zhao

Bibliographic record

VenueZhonghua guke zazhi · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWOMACOsteoarthritisSagittal planeTotal knee arthroplastyRadiographyArthroplastySurgeryNavigation systemRadiology

Abstract

fetched live from OpenAlex

Objective To explore the application value of iASSIST portable navigation in total knee arthroplasty. Methods Seventy-four patients with knee osteoarthritis from April 2016 to April 2017 were retrospectively recruited. Thirty-seven patients (37 knees) underwent TKA with iASSIST navigation, while 37 patients (37 knees) underwent conventional TKA. Five parameters were measured on the weight-bearing radiographs at six months after TKA, including mechanical axis (MA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), sagittal femoral component angle (sFCA) and sagittal tibial component angle (sTCA). Duration of operation, blood loss volume, postoperative hospital day, Western Ontario and McMaster Universities (WOMAC) osteoarthritis index, Knee Society Score (KSS) clinical score and functional score at 6 weeks, 12 weeks and 24 weeks after surgery were also recorded. Results The accuracy of MA (180.85°±0.88° versus 182.23°±1.09° in the conventional group, P 0.05). Conclusion More accurate restoration in mechanical axis and optimal implantation can be achieved with the help of iASSIST navigation. This navigation system can also achieve better knee function in the early stage after TKA. Key words: Osteoarthritis, knee; Arthroplasty, replacement, knee; Surgery, computer-assisted; X-rays

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 score0.053
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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