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Deep-Dish Congruent Tibial Component Use in Total Knee Arthroplasty

2000· article· en· W2013539786 on OpenAlexaff
Richard S. Laskin, Yuichiro Maruyama, Manuel Villaneuva, Robert B. Bourne

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2000
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCoronal planeImplantSagittal planeSurgeryPosterior cruciate ligamentRange of motionTibiaArthroplastyOsteoarthritisAnterior cruciate ligamentPerioperativeTotal knee arthroplastyOrthopedic surgeryOrthodonticsAnatomy

Abstract

fetched live from OpenAlex

One hundred seventy-six patients with osteoarthritis of the knee were randomized prospectively into two groups. In both groups the posterior cruciate ligament was released from its femoral attachment. In one group a posterior stabilized tibial component was used whereas in the other group a deep-dish tibial polyethylene component was inserted (Genesis II). The surgical and perioperative technique was identical in both groups and all the implants were cemented to their respective bones. Patients began range of motion exercises within the first few hours after surgery and were allowed weightbearing to tolerance beginning on the first postoperative day. At followup there was no statistical difference in the mean range of flexion (approximately 116 degrees), ability to ascend and descend stairs in a bipedal manner (80%), pain scores, knee scores (94 points), stability, or the lack of anterior knee pain. Postoperative implant alignment in the sagittal and coronal planes and on Merchant skyline views was excellent in both groups. There was only one lateral release required and that was in one patient who received a deep-dish component. Using deep-dish implant obviates the need to resect intercondylar femoral bone, decreasing the potential for fracture and maximizing bone volume should revision be necessary in the future.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.068
GPT teacher head0.382
Teacher spread0.314 · 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.

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

Citations107
Published2000
Admission routes1
Has abstractyes

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