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Record W2981283467 · doi:10.1177/1120700019881429

Prospective clinical study using radiostereometric analysis (RSA) to evaluate fixation of a modular cemented polished femoral stem

2019· article· en· W2981283467 on OpenAlexaff
Craig Richardson, Elise Laende, Michael L. Gross, Michael Dunbar

Bibliographic record

VenueHip International · 2019
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersWright Medical Group
KeywordsMedicineFixation (population genetics)ImplantProspective cohort studyDentistryFemoral neckOrthopedic surgerySurgeryOrthodonticsOsteoporosis

Abstract

fetched live from OpenAlex

BACKGROUND: The long-term clinical success of cemented polished tapered femoral components has been demonstrated in prospective as well as registry studies. This stem design type has also been well studied with radiostereometric analysis (RSA) and provides a standard to evaluate alternative designs of polished tapered cemented femoral components. This prospective study evaluates the subsidence of a polished cemented stem with a modular neck design utilising RSA. METHODS: 26 patients were prospectively enrolled in the study and 23 were available for RSA analysis at 2 years. RESULTS: The average subsidence of the femoral implant was 1.1 mm (SD 0.4 mm) at 2 years post operation. There were no revisions in this group, specifically no failures of the modular stem-neck design as has been seen with other implants. CONCLUSIONS: The RSA subsidence pattern at 2 years of this cemented femoral stem is similar to other successful designs with long-term follow-up. The implications over time of stem neck modularity for cemented polished tapered femoral components requires further evaluation.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.073
GPT teacher head0.394
Teacher spread0.321 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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