(300) Systematic Review and Meta-analysis of Seasonal Variation on Human Semen Quality in the United States
Bibliographic record
Abstract
Abstract Introduction Different environmental exposures such as daylight hours and temperature influence spermatogenesis, which vary across seasons. However, studies on the variation of semen analysis across different seasons reported inconsistent results. Objective To investigate the seasonality of human semen parameters in the United States through a systematic review and meta-analysis. Methods We systematically searched PubMed, Scopus, and Web of Science for studies on seasonal variation of human semen parameters in the United States from conception to December 11, 2022, without language restriction, and also scrutinized the reference list of included studies for any additional related study. We determined the pooled standardized mean difference (SMD) and its 95% confidence interval (95%CI) between seasons using the random effect model in a case of substantial heterogeneity (I2 index≥50%) and the fixed-effect model in a case of low heterogeneity (I2 index<50%) in STATA version 17. The methodological quality was checked using Newcastle–Ottawa Quality Assessment Scale (NOS). Results Our search yielded 548 studies. After screening, 7 studies on 131,306 semen analyses were included for meta-analysis. All included studies had high methodological quality (NOS ≥ 7). Figure 1 shows the pooled SMD of semen parameters between seasons. Semen analysis in winter had a higher volume than those in spring (p-value=0.010; I2=4.63%). Winter had better semen quality than summer in terms of concentration (p-value=0.014; I2=0%) and total sperm count (p-value=0.044; I2=47.80%). Semen quality was also better in winter than in fall, in terms of volume (p-value=0.001; I2=0%), concentration (p-value=0.016; I2=23.68%), and total sperm count (p-value=0.022; I2=13.95%). Spring had better semen quality than summer in terms of concentration (p-value<0.001; I2=33.74%) and percent of normal morphology (p-value<0.001; I2=0%), and also had higher concentration than fall (p-value<0.001; I2=42.82%). Semen analysis in summer had a lower percentage of total motility than those in fall (p-value=0.045; I2=0%). Conclusions Semen parameters follow a seasonal pattern, with winter and spring showing better sperm quality regarding concentration and total sperm count than other seasons. Disclosure No.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".