A practical guide to diagnosis, management and treatment of testosterone deficiency for Canadian physicians
Bibliographic record
Abstract
The percentage of men receiving appropriate management fortestosterone deficiency syndrome (TDS) is small in comparisonto prevalence estimates. This is despite linkages to cardiovasculardisease, osteoporosis, diabetes, sexual function, sarcopenia, emotionalwell-being and the metabolic syndrome. Furthermore, theavailability of guidelines has not significantly enhanced the care ofTDS patients. A multidisciplinary group of medical experts soughtto improve the management of testosterone-deficient patients byCanadian physicians. This report describes their conclusions anddefines an algorithm for appropriate TDS management.Le pourcentage d’hommes recevant une prise en charge appropriéepour un syndrome de carence en testostérone est faible en comparaisonavec les taux de prévalence évalués, et ce, malgré le lienentre ce syndrome et les maladies cardiovasculaires, l’ostéoporose,le diabète, la fonction sexuelle, la sarcopénie, le bien-être émotionnelet le syndrome métabolique. Par ailleurs, la publicationde guides de pratique n’a pas amélioré de façon significativeles soins offerts aux patients atteints du syndrome de carence entestostérone. Une équipe multidisciplinaire de médecins a tentéd’améliorer la prise en charge des patients atteints de ce syndromepar les médecins canadiens. Le présent rapport décrit leurs conclusionset propose un algorithme de prise en charge.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.138 | 0.049 |
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".