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Record W4295725642 · doi:10.1155/2022/9343951

[Retracted] The Predictive Value of Neutrophil‐Lymphocyte Ratio in Patients with Polycythemia Vera at the Time of Initial Diagnosis for Thrombotic Events

2022· article· en· W4295725642 on OpenAlexaff
Xuekun Wang, Yansong Tu, Mei Cao, Xiaoyan Jiang, Yazhi Yang, Xiaoyan Zhang, Hurong Lai, Huaijun Tu, Jian Li

Post-publication record

NatureRetraction
ReasonConcerns/Issues about Data;Concerns/Issues about Human Subject Welfare;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Lack of IRB/IACUC Approval and/or Compliance;Unreliable Results and/or Conclusions;
Date6/21/2023 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

VenueBioMed Research International · 2022
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsPolycythemia veraMedicinePredictive valueNeutrophil to lymphocyte ratioValue (mathematics)ImmunologyLymphocyteGastroenterologyInternal medicineStatisticsMathematics

Abstract

fetched live from OpenAlex

Objective . To investigate and discuss the predictive value of the neutrophil‐to‐lymphocyte ratio (NLR) in patients with polycythemia vera (PV) at the time of initial diagnosis, as well as its clinical significance in predicting the occurrence of thrombotic events and the progression of future thrombotic events during follow‐ups, with the goal of providing a reference for the early identification of high‐risk PV patients and the early intervention necessary to improve the prognosis of PV patients. Method . A total of 170 patients diagnosed with PV for the first time were enrolled in this study. The risk factors affecting the occurrence and development of thrombotic events in these patients were statistically analyzed. Results . NLR ( P = 0.030), WBC count ( P = 0.045), and history of previous thrombosis ( P < 0.001) were independent risk factors for thrombotic events at the time of initial diagnosis. Age ≥ 60 years ( P = 0.004), NLR ( P = 0.025), history of previous thrombosis ( P < 0.001), and fibrinogen ( P = 0.042) were independent risk factors for the progression of future thrombotic events during follow‐ups. The receiver operating characteristic curve (ROC curves) showed that NLR was more effective in predicting the progression of future thrombotic events than age ≥ 60 years, history of previous thrombosis, and fibrinogen. Kaplan‐Meier survival analysis showed progression‐free survival time of thrombotic events in the high NLR value group (NLR ≥ 4.713) (median survival time 22.033 months, 95% CI: 4.226‐35.840), which was significantly lower compared to the low NLR value group (NLR < 4.713) (median overall survival time 66.000 months, 95% CI: 50.670‐81.330); the observed difference was statistically significant ( P < 0.001). The 60‐month progression‐free survival in the low NLR value group was 58.8%, while it was 32.8% in the high NLR value group. Conclusion . Peripheral blood NLR levels in patients with PV resulted as an independent risk factor for the occurrence of thrombotic events at the time of initial diagnosis and for the progression of future thrombotic events during follow‐ups. Peripheral blood NLR levels at the time of initial diagnosis and treatment had better diagnostic and predictive value for the progression of future thrombotic events in patients with PV than age ≥ 60 years, history of previous thrombosis, and fibrinogen.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.029
GPT teacher head0.356
Teacher spread0.326 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2022
Admission routes1
Has abstractyes

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