7784 Perspectives Of Anabolic Steroid Users On Steroid Use, Side Effects, And Patient Education
Bibliographic record
Abstract
Abstract Disclosure: D.D. Lam: None. M. Potemkin: None. J. Kreutz: None. H. Shah: None. P. Patel: None. P.K. Doyle-Baker: None. Background: Details on first-hand perceived and experienced effects of anabolic steroids (AS) on users are sparse. Furthermore, there is limited information on what type of AS knowledge users possess, irrespective of current published works in AS pharmacology. This study aimed to identify insights of AS users regarding their knowledge and patterns of AS use, commonly experienced side effects, sources used for steroid education, and their unique needs as a population. Study Design: AS users from Calgary, Alberta, Canada of various sexes and ages ≥ 18 were recruited through gyms in Alberta with convenience and snowball sampling techniques. Participant-centered-qualitative research using semi-structured interviews was conducted. Participants discussed their experiences as a user and shared their understanding of AS, details of usage including compounds used, and their commonly experienced or perceived side effects through one-on-one interviews of varying durations. Interviews were coded using NVivo 14 Software prior to thematic analysis. Resulting codes were organized into overarching themes based on researchers' consensus. Results: Seven AS users were confidentially interviewed over Zoom. Several important themes emerged. There existed a wide variety of AS that users have identified, with some of the most commonly mentioned being Anavar, Nolvadex, Testosterone, and Trenbolone. Steroid users frequently identified increased strength with faster recovery from training and better overall appearance as prominent advantages of AS use. Conversely, hair loss, gynecomastia, acne, aggression, erectile dysfunction, altered libido, hypogonadism, infertility, and injection related discomfort were commonly identified disadvantages of AS use. Lastly, individuals using AS report having consulted many different sources to learn about appropriate dosing, techniques, and side effects. These sources ranged from peers, coaches, online forums, social media, and scientific research articles, but rarely included healthcare professionals. Conclusions: This study identified the need for education and research in exploring the impact of AS use. A variety of AS exist on the market, yet information pertaining to the side effect profiles of these AS from a patient’s perspective is lacking and exploration of this topic is warranted. Further investigation as to the impact of user education on their understanding of the risks versus the benefits of AS use must also be conducted. Presentation: 6/1/2024
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".