The likelihood of having a serum PSA level of ≥2.5 ng/mL according to the degree of fatty liver disease in a screened population
Bibliographic record
Abstract
INTRODUCTION: We sought to investigate the impact of fatty liver disease (FLD) on prostate cancer (PCa) screening by estimating the odds of having a prostate-specific antigen (PSA) value over the cutoff used to prompt for the recommendation of prostate biopsy. METHODS: Between 2007 and 2013, 18 533 native Korean men eligible to receive a serum PSA test, liver profiles, and abdominal ultrasonography were recruited. Logistic regression was used to estimate the odds of an abnormal PSA (≥2.5 ng/mL) in these men (age 45-75 years, PSA≤10 ng/mL) in relation to FLD. The FLD status was categorized as normal, mild, moderate, and severe grade by abdominal sonography. RESULTS: A total of 16 563 men (89.4%) were included in the study after applying the inclusion criteria. Liver profiles were negatively correlated with the serum PSA level. After controlling for age and obesity, there was a statistically significant trend towards a lower likelihood of having a serum PSA level of ≥2.5 ng/mL with severe FLD, having a 34.7% lower likelihood (odds ratio 0.653, 95% confidence interval 0.477-0.88; p<0.01) compared to men in the normal group. CONCLUSIONS: Severe FLD is an independent predictor of a lower likelihood of having abnormal PSA level. Further studies are needed to better define these results in clinical biopsy practice.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".