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Record W2113465874 · doi:10.1186/s13643-015-0016-4

Role of preoperative intravenous iron therapy to correct anemia before major surgery: study protocol for systematic review and meta-analysis

2015· article· en· W2113465874 on OpenAlexafffund
Abdelsalam M. Elhenawy, Steven Meyer, Sean M. Bagshaw, Roderick MacArthur, Linda Carroll

Bibliographic record

VenueSystematic Reviews · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesUniversity of Alberta
KeywordsMedicinePerioperativeMeta-analysisCochrane LibraryRandomized controlled trialAdverse effectBlood transfusionIntensive care unitMEDLINEAnemiaIntensive care medicineRelative riskPlaceboBlood managementEmergency medicineSurgeryConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Preoperative anemia is a common and potentially serious hematological problem in elective surgery and increases the risk for perioperative red blood cell (RBC) transfusion. Transfusion is associated with postoperative morbidity and mortality. Preoperative intravenous (IV) iron therapy has been proposed as an intervention to reduce perioperative transfusion; however, studies are generally small, limited, and inconclusive. METHODS/DESIGN: We propose performing a systematic review and meta-analysis. We will search MEDLINE, EMBASE, EBM Reviews, Cochrane-controlled trial registry, Scopus, registries of health technology assessment and clinical trials, Web of Science, ProQuest Dissertations and Theses, and conference proceedings in transfusion, hematology, and surgery. We will contact our study drug manufacturer for unpublished trials. Titles and abstracts will be identified and assessed by two reviewers for potential relevance. Eligible studies are: randomized or quasi-randomized clinical trials comparing preoperative administration of IV iron with placebo or standard of care to reduce perioperative blood transfusion in anemic patients undergoing major surgery. Screening, data extraction, and quality appraisal will be conducted independently by two authors. Data will be presented in evidence tables and in meta-analytic forest plots. Primary efficacy outcomes are change in hemoglobin concentration and proportion of patients requiring RBC transfusion. Secondary outcomes include number of units of blood or blood products transfused perioperatively, transfusion-related acute lung injury, neurologic complications, adverse events, postoperative infections, cardiopulmonary complications, intensive care unit (ICU) admission/readmission, length of hospital stay, acute kidney injury, and mortality. Dichotomous outcomes will be reported as pooled relative risks and 95% confidence intervals. Continuous outcomes will be reported using calculated weighted mean differences. Meta-regression will be performed to evaluate the impact of potential confounding variables on study effect estimates. DISCUSSION: Reducing unnecessary RBC transfusions in perioperative medicine is a clinical priority. This involves the identification of patients at risk of receiving transfusions along with blood conservation strategies. Of potential pharmacological blood conservation strategies, IV iron is a compelling intervention to treat preoperative anemia; however, existing data are uncertain. We propose performing a systematic review and meta-analysis evaluating the efficacy and safety of IV iron administration to anemic patients undergoing major surgery to reduce transfusion and perioperative morbidity and mortality. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015016771.

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)0.041
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.043
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.070
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0240.031
Bibliometrics0.0090.009
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0040.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0430.003

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 head0.110
GPT teacher head0.383
Teacher spread0.274 · 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 designSystematic review
Domainnot available
GenreProtocol

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

Citations29
Published2015
Admission routes2
Has abstractyes

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