When Type 1 Diabetes Meets Dementia: Practical Strategies to Help Patients and Their Loved Ones
Notice bibliographique
Résumé
As the population ages, increasing numbers of people are affected by dementia.Individuals living with diabetes are at particular risk of cognitive decline as they age (1).This fact has been well documented with regard to people living with type 2 diabetes but has also been noted in those living with type 1 diabetes (2).Now, in the 21st century, as more people with type 1 diabetes are living longer (3), we should not be surprised when the need for addressing dementia and other late-age cognitive issues becomes more common in our clinical practices.Certain support strategies have been developed to assist individuals living with type 2 diabetes and dementia, and their caregivers, such as spouses or other loved ones.What has received far less attention, however, is the dilemma confronting individuals living with type 1 diabetes and dementia and, moreover, the sometimes dire challenges confronting their caregivers as they gamely, but often unsuccessfully, try to take on new roles in assisting with diabetes management (4,5). Meet Joanne**Joanne's story is a composite with some features changed to preserve patient confidentiality.Consider the case of Joanne, a 75-year-old woman with longstanding type 1 diabetes who was referred to one of us (I.R.B., or "Dr.B") for an initial consultation.After exchanging the usual introductory greetings and pleasantries, Dr. B asked Joanne how long she had had diabetes."Oh, a long time," she said as she smiled."Uhuh," he probed, "Like 10, 20, 30 years?" "Oh, at least," Joanne replied."Hmm," Dr. B thought.This was a surprisingly vague answer.Most people with type 1 diabetes recall with precision the details surrounding when they were diagnosed."And which insulin are you taking?" he asked."Lantus and Humalog," she quickly answered."Oh, okay, great.Thanks.And how much do you take?" "Ah, well," she hesitated, "it depends.I use a ratio.One unit for every 10 units.""One unit for every 10 units?" he thought."Well, maybe that was an innocent, misspoken comment.""So, how many units does that typically work out to for most of your meals?" he asked.Joanne shrugged, but did not respond."Like, on average," he continued, "would it be 2, 3 units?Or more like 20 or 30 units?" Joanne started to answer, then again fell silent."Joanne," Dr. B asked, "are you here with anybody today?" "Yes," she replied, "my husband, Frank, is here with me."She agreed to have her husband join them."Hi Frank," Dr. B said as Joanne's husband was ushered into the examining room."We were just discussing Joanne's diabetes.She said she's had it for a long time.""Oh yes," Frank replied, "even before we were married, and that was almost 50 years ago."Joanne indicated that it was okay to continue asking Frank questions."Frank," Dr. B said, "just to doublecheck a few things with you, can you confirm which types of insulin Joanne is taking?""I wouldn't know that," he quickly replied."Two types I think.""Oh, okay," Dr. B went on, "and how many units of them does she take?" "Oh, I wouldn't know that either," Frank said."Joanne looks after all of that.Always has." "Joanne," Dr. B said, as he again turned toward her, "how is your diabetes control?Do you know what your most recent A1C was?Or how often you're high or low?That sort of thing?" "Oh, I've got great control," Joanne quickly responded.
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 | 0,005 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,007 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,153 | 0,081 |
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 ».