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Record W3029277608 · doi:10.1177/0300060520925999

Greater increase in femoral offset with use of collum femoris-preserving stem than Tri-Lock stem in primary total hip arthroplasty

2020· article· en· W3029277608 on OpenAlexaboutno aff
Mengxuan Yao, Yuchuan Wang, Congcong Wei, Yongtai Han, Huijie Li

Bibliographic record

VenueJournal of International Medical Research · 2020
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeriprostheticOrthopedic surgeryOsteoarthritisSurgeryTotal hip arthroplastyArthroplasty

Abstract

fetched live from OpenAlex

Objective This study was performed to compare the clinical outcomes and performance of the collum femoris-preserving (CFP) stem (Waldemar Link GmbH & Co., Hamburg, Germany) and the Tri-Lock stem (DePuy Orthopaedics, Warsaw, IN, USA) in terms of femoral offset (FO) and leg length reconstruction. Methods Clinical and radiographic data of patients who underwent total hip arthroplasty with either a CFP stem or Tri-Lock stem from January 2016 to March 2017 were compared (65 and 57 patients, respectively). The Harris hip score and Western Ontario and McMaster Universities Osteoarthritis Index were recorded. The FO, femoral vertical offset, and neck–shaft angle were measured at the last follow-up. The occurrence of dislocation and periprosthetic fracture during the follow-up period was recorded. Results The CFP stem induced significantly more FO than did the Tri-Lock stem on the operated side than contralateral side (3.63 ± 4.28 vs. 0.83 ± 5.46 mm). Significantly fewer patients had a >5-mm decrease in FO on the unaffected side in the CFP stem group ( n = 1) than Tri-Lock stem group ( n = 10). Conclusion Both stems similarly improved hip function and reconstructed the leg length, but the CFP stem was superior to the Tri-Lock stem in reconstructing FO.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.027
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.098
GPT teacher head0.343
Teacher spread0.245 · 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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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