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Record W4417454309 · doi:10.3390/neurolint17120207

RETRACTED: Patel et al. Effect Comparison of E-Cigarette and Traditional Smoking and Association with Stroke—A Cross-Sectional Study of NHANES. Neurol. Int. 2022, 14, 441–452

2025· article· en· W4417454309 on OpenAlexaff
Urvish Patel, Neel Patel, Mahika Khurana, Akshada Parulekar, Amrapali Patel, Juan Fernando Ortíz, Rutul Patel, Eseosa Urhoghide, Anuja Mistry, Arpita Bhriguvanshi, Mohammed Ali Abdulqader, Neev Mehta, Kogulavadanan Arumaithurai, Shamik Shah

Post-publication record

OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.

Bibliographic record

VenueNeurology International · 2025
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsYork University
Fundersnot available
KeywordsAssociation (psychology)Alternative medicineMEDLINECigarette smoking

Abstract

fetched live from OpenAlex

The journal retracts the article, "Effect Comparison of E-Cigarette and Traditional Smoking and Association with Stroke-A Cross-Sectional Study of NHANES" [...].

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptMetaresearchResearch integrity
Domain: Evaluation · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.015
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.087
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0040.004
Science and technology studies0.0060.003
Scholarly communication0.0060.004
Open science0.0150.004
Research integrity0.0180.022
Insufficient payload (model declined to judge)0.0240.016

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 head0.335
Teacher spread0.311 · 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

Labeled directly by 2 models reading the full record.

Research integrityMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Not applicable
DomainEvaluation
GenreEmpirical · Editorial

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

Citations1
Published2025
Admission routes1
Has abstractyes

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