PMON258 Patient-Reported Outcomes for men with Hypogonadism and the Impact of Low Testosterone Levels: a review of measures
Notice bibliographique
Résumé
Abstract Background To better understand the impact of low testosterone treatments on men with hypogonadism, data on treatment efficacy and safety must be combined with patient-reported outcomes measurements (PROMs). Whether these objective tools conceptualise and measure the impacts of hypogonadism in the same way, is not known. Aim To appraise the evidence on the item content of validated patient-reported outcome measures for hypogonadism evaluations and identify core domains of potential importance in this context. Methods We systematically reviewed tools (e.g., questionnaires, surveys, scales) in published quantitative or qualitative data of men with low testosterone and/or those using (or who had considered treatment). PROMs data extraction forms and data tables were generated for each stage of the extraction process to standardise the information recorded and aid analysis. Data was synthesised by classifying the items identified into domains determined by the nomenclature reported in included studies and the International Classification of Functioning, Disability and Health (WHO-ICF). Finally, a narrative synthesis of the instruments and their inter-related domains and subdomains was conducted to identify areas of both convergence and divergence. Results A total of nine tools measuring PROMs of men with low testosterone were included in this review. The included studies were set within the US (n=5), Canada (n=1), UK (n=1), Germany (n=1), Italy (n=1). The tools identified were: Androgen Deficiency in Aging Males (ADAM) Questionnaire, The Aging Males’ Symptoms (AMS) scale, ANDROTEST ©, The Age-Related Hormone Deficiency Dependent Quality of Life Questionnaire (A-RHDQoL)©, Hypogonadism Energy Diary (HED), Hypogonadism Impact of Symptoms Questionnaire (HIS-Q), HIS-Q-Short Form (HIS-Q-SF), Massachusetts Male Ageing Study (MMAS) questionnaire, Sexual Arousal, Interest, and Drive Scale (SAID). Only HED, SAID, and HIS-Q reported including patients while developing the tool. The number of items varied across instruments and ranged from 3 to 53 items (median=7) with a cumulative total of 98 individual items. The ten domains identified were: Cognition, Energy, General well-being, Mood, Pain, Physical-General, Role, Sexual, Sleep, Social. Across tools, the most frequently identified domain was the sexual domain. However, two of the PROMs, HED and MMAS, did not include any items that covered the sexual domain. Six of the nine tools were considered multi-dimensional, and three were considered unidimensional (i.e. only capturing one domain). The A-RHDQoL tool showed to be the most comprehensive tool across the PROMs included since this was the only one to include items that could be coded to all ten domains. Conclusions This study has demonstrated the considerable item concept variability across disease-specific PROMs for men with low testosterone regarding development and domain coverage. The dominant focus of these PROMs to date has centred around sexual function, but possibly to the detriment of other aspects that also matter to patients. Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.
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 | 0,020 | 0,086 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,018 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».