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Record W4390174663 · doi:10.3390/prosthesis6010004

Mid-Term Outcomes of a Modern Zweymüller Monolithic Femoral Stem in Primary Total Hip Arthroplasty

2023· article· en· W4390174663 on OpenAlexaboutno aff
Vincenzo Ciriello, Michela Saracco, Enrico Leonardi, Lucio Piovani, Allison Fetz-Palazola, Chase Mareno, Giandomenico Logroscino

Bibliographic record

VenueProsthesis · 2023
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeriprostheticSurvivorship curveSurgeryHarris Hip ScoreTotal hip arthroplastyRetrospective cohort studyCohortTotal hip replacementArthroplastyInternal medicineCancer

Abstract

fetched live from OpenAlex

Background: Prosthetic hip replacement is a widely practiced surgical procedure with excellent results. Hip stems based on the Zweymüller design have a long history and their philosophy focuses on achieving long-term stability promoting physiological load transfer. The aim of this study is to evaluate outcomes, survivorship, and complication rates in a cohort of patients treated with this stem. Methods: A retrospective review was conducted to identify patients who underwent primary THA with Zweymüller stems. A total of 86 hips (43%) were implanted in men, and 114 hips (57%) in women. The mean follow-up time was 5.4 years. Patient charts were reviewed for adverse events occurring after primary THA as well as for revision and indication for revision. Patients still implanted with the hip stem were asked to complete the HOOS JR survey. Results: Ten hips (5.0%) were lost to follow up. The mean HOOS JR score at final evaluation was 96.3 points (range, 60.0–100.0). Two revisions were performed during the study for femoral stem loosening due to a periprosthetic Vancouver B2 fracture and for multiple hip dislocations. Conclusions: The Zweymüller design hip stem showed excellent survivorship and favorable clinical outcomes at a mean follow-up of 5.4 years.

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 score0.168
Threshold uncertainty score0.712

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.268
Teacher spread0.233 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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