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Record W4405057005 · doi:10.1371/journal.pone.0314565

RETRACTED: Investigating the relationship between insulin use and all-cause mortality, breast cancer mortality, and recurrence risk in diabetic patients with breast cancer: A comprehensive systematic review and meta-analysis

2024· review· en· W4405057005 on OpenAlexaboutno aff
M.V. Loktionova, Mahdi Mohammadian, Roya Choopani, Soleiman Kheiri

Post-publication record

NatureRetraction
ReasonBreach of Policy by Author;Concerns/Issues about Article;Investigation by Journal/Publisher;Objections by Author(s);Unreliable Results and/or Conclusions;
Date12/18/2025 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenuePLoS ONE · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerMeta-analysisInternal medicineCancerOncologyChecklistDiabetes mellitusRelative riskConfidence intervalEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: The co-occurrence of breast cancer and diabetes presents complex clinical challenges, as each condition may influence the progression and management of the other, potentially worsening patient outcomes. This study aims to examine the association between insulin use and the risks of all-cause mortality, breast cancer-specific mortality, and recurrence in diabetic patients with breast cancer. METHODS: A systematic review and meta-analysis were conducted using studies identified from multiple databases, including Web of Science, Scopus, PubMed, Cochrane, Google Scholar, and Embase. The meta-analysis approach was used to estimate the relative risk (RR) of the relationship between insulin use and the risks of all-cause mortality, breast cancer-specific mortality, and recurrence in diabetic patients with breast cancer. Heterogeneity among studies was assessed using statistical tests such as the Chi-square test, I2, and forest plots. Meta-regression and sensitivity analyses were performed to explore sources of heterogeneity. The quality of the included studies was assessed using the Newcastle-Ottawa Scale checklist. Data were analyzed using Stata version 17 (Stata Corp, College Station, Texas). RESULTS: Data from 22 studies conducted between 2002 and 2023, with a total of 159,674 participants, were analyzed. Nineteen studies were rated as high quality, and three as moderate quality. Diabetic patients with breast cancer who received insulin had a 1.65 (95% CI: 1.36-2.02; P < 0.001; I2 = 89.7%) times higher risk of overall mortality compared to those who did not use insulin. Meta-regression revealed that sample size and study quality were significant contributors to heterogeneity (P ≤ 0.10). Furthermore, insulin use was associated with a 1.22 (95% CI: 1.05-1.42; P = 0.009; I2 = 37.9%) times higher risk of breast cancer-specific mortality. For breast cancer recurrence, insulin use was associated with a 1.45 (95% CI: 1.19-1.77; P < 0.001; I2 = 3.4%) times higher risk. Sensitivity analysis confirmed the stability of the results across all outcomes. CONCLUSION: This meta-analysis provides strong evidence that insulin use in diabetic patients with breast cancer is associated with increased risks of overall mortality, breast cancer-specific mortality, and recurrence. These findings underscore the need for careful consideration of insulin therapy in this patient population.

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)0.068
metaresearch head score (Gemma)0.216
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.216
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.014
Bibliometrics0.0080.013
Science and technology studies0.0020.002
Scholarly communication0.0060.007
Open science0.0060.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.272
GPT teacher head0.361
Teacher spread0.089 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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