Systemic bias in the medical literature on androgen deprivation therapy and its implication to clinical practice
Bibliographic record
Abstract
BACKGROUND: LHRH agonists are used for androgen deprivation therapy (ADT) to treat prostate cancer, but have many side effects that reduce of the quality of life of prostate cancer patients and their partners. Patients are poorly informed about the side effects of these drugs and how to manage them. AIM: To test the hypothesis that there is bias in the peer-reviewed literature on ADT that correlates with an association between authors and the luteinising hormone-releasing hormone (LHRH) agonists pharmaceutical industry. METHODS: We assessed 155 articles on ADT published in English-language peer-reviewed journals in terms of how comprehensive they were in acknowledging LHRH agonists' side effects. RESULTS: Although the literature regarding ADT is substantial, the vast majority of articles failed to acknowledge many of the more stressful side effects of ADT for patients and their partners. Articles most likely to acknowledge the psychosocial impact of ADT were significantly less likely to have had industrial support than those articles that did not mention those side effects. Alternative treatments to the LHRH agonists were rarely mentioned. Authors who indicated some association with a pharmaceutical company tended to minimise the side effects of LHRH agonists and not acknowledge alternatives to the LHRH agonists for ADT. CONCLUSION: Industrial support is associated with a proliferation of articles published in the peer-reviewed literature directed at practising physicians. Such flooding of the literature may, in part, limit physicians' knowledge of the side effects of these drugs and, in turn, account for the poor knowledge that patients on LHRH agonists have about the drugs they are taking and ways to manage their side effects.
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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 | 0.331 | 0.752 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.036 | 0.029 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".