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Record W2009336528 · doi:10.14740/jcs236w

The Effect of Gender, Age and BMI on Postoperative C-Reactive Protein Levels After Major Abdominal Surgery

2014· article· en· W2009336528 on OpenAlexvenueno aff
Jennifer Straatman

Bibliographic record

VenueJournal of Current Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryObservational studyC-reactive proteinAbdominal surgeryComplicationMedical recordRetrospective cohort studyInflammationInternal medicine

Abstract

fetched live from OpenAlex

Background: The aim of this observational study was to analyze whether differences exist in postoperative C-reactive protein (CRP) levels in elderly, obese or for gender after major abdominal surgery, which might affect the diagnostic value of CRP. Methods: Observational cohort study was made between January 2009 and January 2011 of all adult patients who underwent major abdominal surgery. Medical records and radiology were reviewed. Complications were recorded based on the classification by Clavien-Dindo. CRP data were collected up to 14 days postoperatively, or until discharge. Results: Three hundred ninety-nine patients underwent major abdominal surgery. Seventy-four patients underwent upper gastrointestinal (GI) surgery, 91 patients underwent hepato-pancreatico-billiary (HPB) surgery and 234 underwent lower GI surgery. Two hundred thirty-five patients were male. Eighty-three patients presented with a major complication (20.8%). No effect of gender or age on postoperative CRP levels was observed. Although a positive correlation was observed for BMI, this did not hold in further regression analysis. Conclusions: No effect was observed for gender, age and BMI on postoperative CRP levels. It is proposed that the effect of surgical trauma and inflammation surpasses the effect of these related patient factors. The results further support the use of CRP as an independent marker of postoperative inflammation and complications. J Curr Surg. 2014;4(3):61-69 doi: http://dx.doi.org/10.14740/jcs236w

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 integrity0.0000.000
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 head0.030
GPT teacher head0.298
Teacher spread0.268 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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