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Record W2905077084 · doi:10.1177/2192568218799056

Surgical Site Infection in Spine Surgery: Who Is at Risk?

2018· article· en· W2905077084 on OpenAlexaff
Reina Yao, Hanbing Zhou, Theodore J. Choma, Brian K. Kwon, John Street

Bibliographic record

VenueGlobal Spine Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicSurgical site infection prevention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineSurgerySurgical site infectionSpinal surgerySPINE (molecular biology)Bioinformatics

Abstract

fetched live from OpenAlex

STUDY DESIGN: Retrospective literature review of spine surgical site infection (SSI). OBJECTIVE: To perform a review of SSI risk factors and more specifically, categorize them into patient and surgical factors. METHODS: A review of published literature on SSI risk factors in adult spine surgery was performed. We included studies that reported risk factors for SSI in adult spinal surgery. Excluded are pediatric patient populations, systematic reviews, and meta-analyses. Overall, we identified 72 cohort studies, 1 controlled-cohort study, 1 matched-cohort study, 1 matched-paired cohort study, 12 case-controlled studies (CCS), 6 case series, and 1 cross-sectional study. RESULTS: ), subcutaneous fat thickness, multiple medical comorbidities, current smoker, and malnutrition were associated with SSI. Surgical associated factors-preoperative radiation/postoperative blood transfusion, combined anterior/posterior approach, surgical invasiveness, or levels of instrumentation were associated with increased SSI. There is mixed evidence of age, duration of surgery, surgical team, intraoperative blood loss, dural tear, and urinary tract infection/urinary catheter in association with SSI. CONCLUSION: SSIs are associated with many risk factors that can be patient or surgically related. Our review was able to identify important modifiable and nonmodifiable risk factors that can be essential in surgical planning and discussion with patients.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.310
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

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

Citations137
Published2018
Admission routes1
Has abstractyes

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