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Record W3134065413 · doi:10.1093/jcag/gwab002.113

A115 A SYSTEMATIC REVIEW AND META-ANALYSIS OF LOWER GASTROINTESTINAL BLEEDING RISK SCORES TO PREDICT ADVERSE OUTCOMES

2021· review· en· W3134065413 on OpenAlexaff
Majed Almaghrabi, M Gandhi, Leonardo Guizzetti, Alla Iansavitchene, Kathryn Oakland, Vipul Jairath, Michael Sey

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2021
Typereview
Languageen
FieldMedicine
TopicGastrointestinal Bleeding Diagnosis and Treatment
Canadian institutionsLondon Health Sciences CentreGrand River HospitalWestern University
Fundersnot available
KeywordsMedicineMeta-analysisReceiver operating characteristicMEDLINESystematic reviewLower gastrointestinal bleedingColonoscopyInternal medicineColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background Acute lower gastrointestinal bleeding (LGIB) is a common reason for emergency hospitalization. In most patients, bleeding resolves spontaneously but some cases can be fatal. Risk prediction scores can be useful in risk stratifying patients with LGIB at the time of presentation although the most discriminative LGIB risk score is unknown. Aims To perform a systematic review and meta-analysis comparing LGIB risk prediction scores. Methods Following the PRISMA statement, a systematic search for relevant publications after 1990 was conducted in Ovid Medline, EMBASE, Web of Science and CENTRAL electronic databases. We also searched published conference abstracts over the past 5 years. Studies with a primary aim of deriving or validating a LGIB risk score were included. Title and abstracts were reviewed by two independent reviewers followed by full text review and data extraction by both reviewers. Diagnostic classification data for combinations of risk score and clinical outcome were meta-analyzed using a hierarchical summary receiver operator characteristic curve (ROC) model, allowing for random-effects by study, and fixed-effect of the risk score thresholds to influence both sensitivity and specificity. Area under the summary ROC were estimated from model parameters for the pre-specified LGIB risk score thresholds-of-interest. Results Our search identified 2,331 citations for review, of which 100 remained after the title and abstract screen, and 18 ultimately met criteria for inclusion in the meta-analysis after full text review. From these, we identified 21 risk prediction scores for LGIB, although only four had sufficient number of papers to meta-analyze (Oakland, Strate, NOBLADS, and BLEED score). For the outcome safe discharge from hospital, the Oakland score had an area under the receiver operating characteristics curve (AUROC) of 85.5% (95% CI: 82.1%, 88.3%). For the outcome major bleeding, the Oakland score had an AUROC of 78.9% (95% CI: 75.1%, 82.2%); the Strate score had an AUROC of 74.4% (95% CI: 70.4%, 78.0%); the NOBLADS score had an AUROC of 60.3% (95% CI: 55.9%, 64.5%); and the BLEED score had an AUROC of 65.6% (95% CI: 61.4%, 69.7%). For the outcome, need for hemostasis, the Oakland had an AUROC of 99.0% (95% CI: 97.7%, 99.6%); the Strate score had an AUROC of 82.1% (95% CI: 78.5%, 85.2%); the NOBLADS score had an AUROC of 23.9% (95% CI: 20.3%, 27.8%). For the outcome, need for transfusion, the Oakland score had an AUROC of 99.0% (95% CI: 97.7%, 99.6%); the NOBLADS score had an AUROC of 87.7% (95% CI: 84.5%, 90.3%). Conclusions The Oakland score was the most discriminative risk prediction model for safe discharge from hospital, major bleeding, need for hemostasis, and need for transfusion. Funding Agencies None

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)0.018
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.056
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.037
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.039
GPT teacher head0.308
Teacher spread0.269 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Canadian Association of GastroenterologySame topicGastrointestinal Bleeding Diagnosis and TreatmentFrench-language works237,207