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Record W2795418908 · doi:10.21037/map.2018.ab135

AB135. 33. Role of trabecular metal augments for Paprosky type 3 defects in acetabular revision

2018· article· en· W2795418908 on OpenAlexaboutno aff
Cathleen J. O’Neil, Stephen B. Creedon, Stephen Brennan, Fiona J. O’Mahony, Rose Lynham, Shane Guerin, Rehan Gul, James A. Harty

Bibliographic record

VenueMesentery and Peritoneum · 2018
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWOMACAcetabulumSurgeryRadiographyUrologyOsteoarthritisPathology

Abstract

fetched live from OpenAlex

Background: Trabecular metal augments are one option when reconstructing bone loss during acetabular side revision surgery. Methods: We studied 38 consecutive patients with Paprosky type 3 defects that were revised using a Trabecular Metal shell and one or more augments over a 6-year period. There were 29 Paprosky type 3A defects and 9 Paprosky type 3B defects. The mean age of the patients at time of surgery was 68.2 years (range, 48–84 years). The mean length of follow-up was 36 months (range, 18–74 months). Results: The mean pre-operative SF12 improved from 27.7 before operation to 30.1 at the time of final follow-up (P=0.001). The mean Western Ontario and McMaster Universities Arthritis Index (WOMAC) score improved from 53 pre-operatively to a mean of 78.8 at final follow-up (P<0.0001). There was evidence of radiographic loosening in seven of the cup-augment constructs. One patient developed a deep infection requiring re-revision. Two patients required revision for aseptic loosening. Conclusions: The use of Trabecular Metal augments in complex acetabular reconstruction is associated with good outcome in the short to medium term.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.280
Teacher spread0.265 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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