Response: Re: The Influence of Statin Medications on Prostate-Specific Antigen Levels
Notice bibliographique
Résumé
We thank Drs Cicero, Derosa, and Gaddi for their insightful comments regarding our recent study. They asked how the results would be different if we excluded non-simvastatin users, who made up only 5% of our cohort. When this was done, the observed median prostate-specific antigen (PSA) decline increased slightly from 4.1% to 4.5%, though the association with PSA decline and statin dose, and low-density lipoprotein (LDL) decline were not substantially altered. Whether similar results would be obtained with more hydrophilic statins is unknown, and unfortunately, as less than 1% of our cohort used hydrophilic statins, we did not have the power to compare these two groups. Another question that was raised regarded the fact that our results may be confounded by outliers in the effect of statins on LDL cholesterol. After excluding 42 men (3% of study population) with LDL change outside of 2 SDs from the mean change in LDL (26% decline), the overall median PSA decline remained 4.1%. Moreover, the association between decline in LDL and decline in PSA was slightly strengthened. When examining all men, a 10% decline in LDL was associated with a multivariate-adjusted 1.64% decline in PSA ( P = .001), whereas after excluding the 42 outliers, a 10% decline in LDL was associated with a 2.03% multivariate-adjusted decline in PSA ( P < .001). Cicero and colleagues suggested that for some men, possible lifestyle changes may account for the PSA decline. We absolutely agree that lifestyle changes could influence LDL concentrations. However, change in body mass index (BMI) after starting a statin, which may serve as a marker for lifestyle changes, did not confound the relationship between statin use and PSA. Furthermore, LDL change was strongly associated with statin dose. Thus, we feel LDL change serves as an accurate marker of adherence to and efficacy of statins in our cohort. To address this more fully, we stratified patients by BMI at the time of starting a statin and found that within each strata of BMI, statin use was associated with PSA decline, with no significant differences among strata of BMI. Unfortunately, we did not have data on the prevalence of metabolic syndrome in our cohort. Finally, Cicero and colleagues suggested that we should control for nonsteroidal anti-inflammatory drug (NSAID) use. This is an excellent point. Unfortunately, ascertaining NSAID use, many of which are nonprescription medications, using the Veterans Affairs (VA) prescription database proves difficult. Although some men do purchase their NSAIDs through the VA, this is certainly an undercount of those on NSAIDs. However, as the change in PSA in the period before statin use was 0% as compared with a 4.1% decline after starting statins, unless an NSAID was started exactly at the time a statin was started, it would be unlikely that chronic NSAID use could explain the temporal association between starting a statin and PSA decline we observed. Furthermore, the observed association between PSA decline and statin dose and LDL decline makes it unlikely that our results could be explained by NSAID confounding.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,023 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 0,028 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».