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Effect of Changing Indications and Techniques on Total Hip Resurfacing

2007· article· en· W142496979 on OpenAlexaff
Michael A. Mont, Thorsten M. Seyler, Slif D. Ulrich, Paul E. Beaulé, Harold Boyd, Michael J. Grecula, Victor M. Goldberg, William R. Kennedy, David R. Marker, Thomas P. Schmalzried, Edward A. Sparling, Thomas P. Vail, Harlan C. Amstutz

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2007
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineHip resurfacingSurgeryCohortFemoral neckComplicationProspective cohort studySports medicineCohort studyRetrospective cohort studyTotal hip replacementArthroplastyPhysical therapyInternal medicineOsteoporosis

Abstract

fetched live from OpenAlex

Recently, improved metal-on-metal bearing technology has led to the reemergence of resurfacing as a reasonable option for total hip arthroplasty. During the course of a prospective multicenter FDA-IDE evaluation of metal-on-metal total hip resurfacings, we modified our indications and emphasized surgical technique where the femoral surface area was small due to femoral cysts and small component size. We assessed the influence of these changes on complication rates in the first cohort of 292 patients and the second of 724, and then compared these outcomes in the second cohort with historical reports of resurfacing. We had a minimum followup of 24 months (mean, 33 months; range, 24-60 months). After changes were made in the indications and technique, the overall complication rate decreased from 13.4% to 2.1% with the femoral neck fracture rate reduced from 7.2% to 0.8%. The outcomes of the second cohort compare with modern-day resurfacing devices and appear superior to historical results. The data suggest patients should be carefully selected and technique optimized to reduce complications. Long-term followup is required to see if these promising results will be maintained.

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)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.444
Teacher spread0.394 · 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

Citations124
Published2007
Admission routes1
Has abstractyes

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