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Record W2082235284 · doi:10.1086/502564

Hospital-Related Determinants For Surgical-Site Infection Following Hip Arthroplasty

2005· article· en· W2082235284 on OpenAlexaff
Eveline Geubbels, J. Wille, Nico Nagelkerke, Christina M. J. E. Vandenbroucke‐Grauls, Diederick E. Grobbee, Annette S. de Boer

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2005
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsSurgical site infectionMedicineHip arthroplastyArthroplastyTotal hip arthroplastySurgeryGeneral surgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine hospital-related risk factors for surgical-site infection (SSI) following hip arthroplasty. DESIGN: Prospective, multicenter cohort study based on surveillance data and data collected through a structured telephone interview. With the use of multilevel logistic regression, the independent effect of hospital-related characteristics on SSI was assessed. SETTING: Thirty-six acute care hospitals in the Dutch surveillance network for nosocomial infections (PREZIES), from 1996 to 2000. PATIENTS: Thirteen thousand six hundred eighty patients who underwent total or partial hip arthroplasty. RESULTS: A high annual volume of operations was associated with a reduced risk of SSI (risk-adjusted risk ratio [RR] per 50 extra operations, 0.85; 95% confidence interval [CI95], 0.74-0.97). With each extra full-time-equivalent infection control staff member per 250 beds available for prevention of SSI, the risk for SSI was decreased (RR, 0.48; CI95, 0.16-1.44), although the decrease was not statistically significant. Hospital size, teaching status, university affiliation, and number of surgeons and their years of experience showed no important association with the risk of SSI. CONCLUSION: Undergoing surgery in a hospital with a low volume of operations increases a patient's risk of SSI.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.309
Teacher spread0.294 · 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.

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

Citations43
Published2005
Admission routes1
Has abstractyes

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