A population study of fasting time and serum prostate-specific antigen (PSA) level
Bibliographic record
Abstract
Prostate cancer is one of the most common cancers in men. Traditional screening and diagnostic methods include digital rectal examinations (DREs), biopsies and serum prostate-specific antigen (PSA) tests, with the latter being the more popular. PSA is a biomarker for prostate cancer; however, it is highly sensitive to external factors as well as other prostate diseases. As such, the reliability of of the serum PSA level as a sole screening and diagnostic tool for prostate cancer is controversial. Recently, it has been shown that fasting extremes can affect concentrations of serum chemistry analytes, thus raising the question of whether or not fasting has an effect on the highly sensitive PSA biomarker. Patients testing for serum PSA levels are often concomitantly submitting to other tests that require fasting, subjecting certain patients to a fasting PSA level while others not. The objective of this study was to investigate whether this discrepancy in fasting state translates into an effect on serum PSA levels. Serum PSA levels and fasting time records for 157 276 men who underwent testing at Calgary Laboratory Services (CLS; Calgary, Alberta, Canada) between 01 January 2010 and 31 March 2013 were accessed. Linear regression models of mean PSA levels and fasting times revealed a statistically important relationship at certain fasting times. Applying a dynamic mathematical model to explore the clinical effect of fasting suggests minimal impact on serum PSA result interpretation. Thus, patients can be tested for serum PSA levels regardless of their fasting state.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".