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Record W3095564124 · doi:10.1136/bmjopen-2019-036592

Preoperative iron treatment in anaemic patients undergoing elective total hip or knee arthroplasty: a systematic review and meta-analysis

2020· review· en· W3095564124 on OpenAlexaboutno aff
Ashley Scrimshire, Alison Booth, Caroline Fairhurst, Alwyn Kotzé, Mike Reed, Catriona McDaid

Bibliographic record

VenueBMJ Open · 2020
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisArthroplastyTotal knee arthroplastyTotal hip arthroplastySurgeryHip arthroplastySystematic reviewGeneral surgeryMEDLINEPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Objectives Preoperative anaemia is associated with increased risks of postoperative complications, blood transfusion and mortality. This meta-analysis aims to review the best available evidence on the clinical effectiveness of preoperative iron in anaemic patients undergoing elective total hip (THR) or total knee replacement (TKR). Design Electronic databases and handsearching were used to identify randomised and non-randomised studies of interventions (NRSI) reporting perioperative blood transfusion rates for anaemic participants receiving iron before elective THR or TKR. Searches of CENTRAL, MEDLINE, Embase, PubMed and other databases were conducted on 17 April 2019 and updated on 15 July 2020. Two investigators independently reviewed studies for eligibility and evaluated risk of bias using the Cochrane risk of bias tool for randomised controlled trials (RCTs) and a modified Newcastle-Ottawa scale for NRSIs. Data extraction was performed by ABS and checked by AB. Meta-analysis used the Mantel-Haenszel method and random-effects models. Results 807 records were identified: 12 studies met the inclusion criteria, of which 10 were eligible for meta-analyses (one RCT and nine NRSIs). Five of the NRSIs were of high-quality while there were some concerns of bias in the RCT. Meta-analysis of 10 studies (n=2178 participants) showed a 39% reduction in risk of receiving a perioperative blood transfusion with iron compared with no iron (risk ratio 0.61, 95% CI 0.50 to 0.73, p<0.001, I 2 =0%). There was a significant reduction in the number of red blood cell units transfused with iron compared with no iron (mean difference −0.37units, 95% CI −0.47 to -0.27, p<0.001, I 2 =40%); six studies (n=1496). Length of stay was significantly reduced with iron, by an average of 2.08 days (95% CI −2.64 to −1.51, p<0.001, I 2 =40%); five studies (n=1140). Conclusions Preoperative iron in anaemic, elective THR or TKR patients, significantly reduces the number of patients and number of units transfused and length of stay. However, high-quality, randomised trials are lacking. PROSPERO registration number CRD42019129035.

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)0.011
metaresearch head score (Gemma)0.024
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.039
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.145
GPT teacher head0.418
Teacher spread0.273 · 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

Citations24
Published2020
Admission routes1
Has abstractyes

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