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Record W2013321513 · doi:10.4329/wjr.v6.i5.218

Factors influencing the yield of mesenteric angiography in lower gastrointestinal bleed

2014· article· en· W2013321513 on OpenAlexaff
Pasteur Rasuli

Bibliographic record

VenueWorld Journal of Radiology · 2014
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineAngiographyBlood pressureBleedIntensive care unitRadiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

AIM: To assess if certain triaging rules could be established to optimize the yield of mesenteric angiography. METHODS: Medical records of 101 patients were retrospectively reviewed and parameters relating to age, gender, pulse rate, blood pressure, serum hemoglobin, intensive care unit (ICU) admission, and the number of packed red blood cells (PRBC) transfused in the 12 and 24 h prior to the angiography were tabulated in two groups with positive and negative angiography results. RESULTS: We found no correlation between gender, pulse rate, blood pressure or serum hemoglobin and positivity of the mesenteric angiogram. But patients with positive angiogram were found to be on average 7 years older (73.2 years vs 65.9 years old) (P = 0.02). Angiogram was positive in 39.3 % (11/28) of patients admitted in ICU vs 23.2% (17/73) who were admitted elsewhere in the hospital (P = 0.03). In the 12 and 24 h prior to angiography, patients with a positive angiogram received a mean of 2.7 ± 2.3 and 3.3 ± 2.6 units of PRBC s respectively, while patients with a negative angiogram had a mean of 1.6 ± 1.9 (P = 0.02) and 2.1 ± 2.6 units (P = 0.04) received respectively in the same period. CONCLUSION: Older age, ICU admission, having received at least 4 units PRBC over 12 h or 5 units over 24 h prior to angiogram are leading indicators for a positive study.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2014
Admission routes1
Has abstractyes

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