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Record W2489582268

FEMORAL NECK MODULARITY: A BRIDGE TOO FAR – AFFIRMS

2013· article· en· W2489582268 on OpenAlexaff
Michael Dunbar

Bibliographic record

VenueJournal of Bone and Joint Surgery-british Volume · 2013
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsModularity (biology)Modular designTotal hip arthroplastyImplantSurgeryArthroplastyMedicineComputer scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

The proximal modular neck in total hip arthroplasty is not a new concept, but there has been a recent resurgence in interest with multiple companies offering proximal modularity. Proponents of neck modularity suggest that inherent advantages include improved soft tissue balancing and decreased risk of dislocation, particularly in cases with difficult anatomy. Favourable results have been reported in DDH and other cases with excessive femoral anteversion, for example. There are numerous theoretical and published negative aspects of proximal neck modularity that should be considered. Modular necks can be an additional source of corrosion and fretting, and specific systems have been recalled over such concerns. There are case reports of dissociation and fracture at the junction. Fracture appears to be a significant issue in some systems. Retroversion of the neck to reduce the chance of dislocation is not necessarily benign with respect to implant fixation and stability, with RSA data suggesting caution in the application of retroverted necks. Modular necks are difficult to dissociate when in vivo , negating the long-term benefit of modular conversion. Finally, proximal neck modularity significantly increases the cost of the implant, without any documented improvement in long-term outcome. Modular necks offer limited advantages with significant potential downside. On balance of the evidence, the routine use of modular necks in primary total hip arthroplasty is difficult to justify.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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