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Record W2046957849 · doi:10.5489/cuaj.2113

Does varicocele correction lead to normalization of preoperatively elevated mean platelet volume levels?

2015· article· en· W2046957849 on OpenAlexvenueno aff
Soner Çoban, İbrahim Keleş, İsmail Bıyık, Muhammed Güzelsoy, Ali Rıza Türkoğlu, N. Ocak

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsVaricoceleMedicineMean platelet volumeMean corpuscular volumeProspective cohort studyUrologySurgeryInternal medicinePlateletHematocritInfertility

Abstract

fetched live from OpenAlex

INTRODUCTION: There are several studies on the relationship between increased mean platelet volume (MPV) and varicocele. We investigated the relationship between preoperative and 6-month postoperative MPV values in patients whose varicocele was corrected with surgery. METHODS: A total of 282 patients underwent surgery at our urology clinic between December 2011 and December 2013 for primary varicocele. We retrospectively examined the records of 61 patients who came to the 6-month postoperative follow-up. The preoperative varicocele diagnosis was made with physical examination findings and supported with colour Doppler ultrasonography. RESULTS: The varicocele was grade I in 12 patients, grade II in 34 patients and grade III in 11 patients. When the preoperative and 6-month postoperative haemoglobin (Hb), MPV, mean corpuscular volume, platelet, and platelet distribution width (PDW) values were compared, there was a significant decrease in MPV (p = 0.019), and a significant increase in Hb (p < 0.001). A noticeable increase was also present in PDW, but it was not statistically significant (p = 0.058). CONCLUSION: We found that MPV increased in patients with varicocele and tended to decrease again after the varicocele was surgically corrected. However, we feel larger prospective series are needed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.242
Teacher spread0.221 · 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 teacher head, 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

Citations22
Published2015
Admission routes1
Has abstractyes

Explore more

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