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Record W2263268301

Deep infection in total hip arthroplasty.

2008· article· en· W2263268301 on OpenAlexaff
Henry Hamilton, John Jamieson

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsLakehead University
Fundersnot available
KeywordsMedicineIncidence (geometry)Erythrocyte sedimentation rateSurgeryInfection rateArthroplastyProspective cohort studyRisk factorWound infectionTotal hip arthroplastyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To report on a 30-year prospective study of deep infection in 1993 consecutive total hip arthroplasties performed by a single surgeon. METHODS: The relations of numerous variables to the incidence of deep infection were studied. RESULTS: The cumulative infection rate after the index total hip arthroplasties rose from 0.8% at 2 years to 1.4% at 20 years; 9.6% of the index operations required further surgery. When infections attributed to these secondary procedures were included, the infection rate rose from 0.9% at 2 years to 2% at 20 years. Although the usual variables increased the incidence of infection, the significant and most precise predictors of infection were radiologic diagnoses of upper pole grade III and protrusio acetabuli, an elevated erythrocyte sedimentation rate, alcoholism and units of blood transfused. CONCLUSION: From 2-20 years, the incidence of deep infection doubled. Preoperative recognition of the first 4 risk factors permits the use of additional prophylactic measures. Spinal or epidural anesthesia reduced the units of blood transfused (the fifth risk factor) and, hence, the risk of infection. Although most deep infections are seeded while the wound is open, there are many possible postoperative causes. In this study, fewer than one-third of the infections that presented after 2 years were related to hematogenous spread. The efficacy of clean air technology was supported, and it is recommended that all measures that may reduce the incidence of deep infection be employed.

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 score0.104
Threshold uncertainty score0.257

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 head0.019
GPT teacher head0.228
Teacher spread0.210 · 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

Citations52
Published2008
Admission routes1
Has abstractyes

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