5‐α reductase inhibitors and the risk of anaemia among men with benign prostatic hyperplasia: A population‐based cohort study
Bibliographic record
Abstract
5-α reductase inhibitors (5αRIs) are effective for the treatment of benign prostatic hyperplasia (BPH). However, 5αRIs could lower levels of haemoglobin, increasing the risk of anaemia. The objective of this study was to compare the rate of anaemia between new users of 5αRIs and α-blockers in the UK. METHODS: We conducted a matched, active comparator, new-user cohort study using the Clinical Practice Research Datalink. The study population consisted of men aged ≥40 years with incident BPH who initiated 5αRIs between 1998 and 2019 and were matched 1:1 on propensity score to new users of α-blockers. Anaemia was defined by a measured haemoglobin <130 g/L. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for anaemia. RESULTS: Our study cohort included 9429 new users of 5αRIs and 9429 matched new users of α-blockers. Their median durations of follow-up were 136 days (interquartile range: 54-336 d) and 77 days (interquartile range: 58-236 d), respectively. A total of 2865 5αRIs users and 2407 α-blocker users developed incident anaemia, representing rates of 37.3 (95% CI: 33.6-41.3) and 42.0 (95% CI: 38.1-46.2) per 100 person-years, respectively. The use of 5αRIs was not associated with an increased risk of anaemia compared to the use of α-blockers (HR: 0.95, 95% CI: 0.90-1.00). Similarly, we did not observe an increased risk of mild, moderate, or severe anaemia. CONCLUSION: The use of 5αRIs was not associated with an increased risk of anaemia compared to the use of α-blockers among men with BPH.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".