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Record W4396518240 · doi:10.1111/andr.13638

Intra‐individual changes in sperm parameters and total motile count with time among infertile men

2024· article· en· W4396518240 on OpenAlexaff
Gilad Karavani, Bader Akroof, Susan Lau, Kirk Lo, Ethan D. Grober, Vrati M. Mehra, Mohamed S. Kattan, Katherine Lajkosz, Keith Jarvi

Bibliographic record

VenueAndrology · 2024
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsSemenSpermInterquartile rangeSemen qualitySperm motilitySemen analysisAndrologyBiologyMedicineInfertilityCohortInternal medicinePregnancy

Abstract

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BACKGROUND: Paternal age association with sperm parameters has been previously studied, demonstrating a decrease in semen volume, sperm motility, and sperm morphology, but not in sperm concentration. However, scarce data exists on the individual intra-personal changes in semen parameters with time. STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To evaluate the changes in semen parameters and total motile count of infertile men over time. MATERIALS AND METHODS: In this retrospective cohort study, infertile men without known risk factors for sperm quality deterioration and at least two semen analyses done > 3 months apart, between 2005 and 2021, were evaluated. Allocation to groups was according to time between first and last semen analyses - 3-12 months, 1-3 years, 3-5 years, and > 5 years. Basic characteristics and first and last semen analyses were compared. The primary outcome was the change in sperm parameters and the secondary outcome was the occurrence of a total motile count < 5 million in men with an initial total motile count > 10 million. RESULTS: A total of 2018 men were included in the study. The median age at first semen analyses was 36.2 (interquartile range: 32.8-40.1) years and the median time between semen analyses was 323 days (range 90-5810 days). The overall trend demonstrated an increase in concentration in the 3-12 months and the 1-3 years groups, whereas volume, motility, and morphology remained similar in these time groups. Semen analyses done more than 5 years apart showed decreased volume (p < 0.05), motility (p < 0.05) morphology (p < 0.05), and steady sperm concentration. Significant declines in TMCs were found over time (p < 0.001), with 18% and 22% of infertile men with an initial total motile count > 10 million dropping to < 5 million after 3 and 5 years, respectively. The factors independently predictive of total motile count < 5 M in the last semen analyses in men with an initial total motile count of > 10 M in a multivariate logistic regression model were baseline volume (odds ratio 0.80, p = 0.03), baseline total motile count (odds ratio 0.98, p = 0.01) and time between semen analyses - 3-5 years (odds ratio 3.79, p < 0.001) and > 5 years (odds ratio 3.49, p = 0.04) DISCUSSION: Our study demonstrates, at the individual level, that while improvement in sperm concentration is observed in the first year and between 1 and 3 years, possibly due to fertility treatments, fertility-related counseling, and lifestyle changes, semen parameters decline with time over 3 years in individuals. Of significance, close to 22% of men with an initial total motile count > 10 million (a range where spontaneous pregnancy is attainable) declined to < 5 million (a range usually indicating a need for in-vitro fertilization/intracytoplasmic sperm injection) over 5 years. This data could contribute to individualized family planning for infertile men regarding the mode and timing of conception and the need for sperm banking, in order to minimize the need for future fertility treatments.

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)0.002
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.222
Teacher spread0.212 · 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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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