semaglutide weight loss patches GLP-1 use: More people without diabetes use drugs for In the gray world of

Diabetes Care (2022) 45(Suppl 1):S1259

Always verify current formulary status before advising patients on whether Medicaid covers weight loss shots in their specific plan

Mekureyaw MF, Pandey C, Hennessy RC, Nicolaisen MH, Liu F, Nybroe O, et al

Food and Drug Administration F1: F1-score (harmonic mean of precision and recall) FastText: Subword-aware word embeddings GAT: Graph attention network GATE: General architecture for text engineering GIT: Vision-language transformer for image-text tasks GLP-1 RA: Glucagon-like peptide-1 receptor agonist GloVe: Global vectors for word representation GRU: Gated recurrent unit kNN: k -nearest neighbors LDA: Latent Dirichlet allocation LLM: Large language model LN(S): Layer normalization (and variants) LR: Logistic regression LSTM: Long short-term memory MCEM: Monte Carlo expectation-maximization (signal detection) MedDRA: Medical dictionary for regulatory activities MedLEE: Medical language extraction and encoding system ML: Machine learning NB: Naive Bayes NER: Named entity recognition Node2Vec: Biased random-walk graph embedding NLP: Natural language processing P: Precision P@10: Precision at rank 10 PCA: Principal component analysis PSB2016: 2016 Patient Safety Benchmark dataset (Twitter) PubMedBERT: BERT pretrained solely on PubMed QA: Question answering R: Recall RAG: Retrieval-augmented generation RF: Random forest RE: Relation extraction RNN: Recurrent neural network RoBERTa: Robustly optimized BERT pretraining approach RoBERTuito: Spanish Twitter-pretrained RoBERTa RUS: Random under-sampling SAGE: Sparse additive generative model SBERT: Sentence-BERT (sentence embeddings) SciBERT: BERT pretrained on scientific text SDNE: Structural deep network embedding SMM4H: Social media mining for health (shared task) SMOTE: Synthetic minority over-sampling technique SRS: Spontaneous reporting system SVM: Support vector machine STS: Semantic textual similarity TF-IDF: Term frequency-inverse document frequency TwiMed: Twitter + PubMed ADE corpus TwitterADR: Twitter adverse drug reaction dataset UMLS: Unified medical language system VAERS: Vaccine adverse event reporting system VADER: Valence Aware Dictionary and sEntiment Reasoner VQC: Variational quantum circuit VUE: Under-sampling variant used in imbalanced learning pipelines WESMOTE: Word-embedding-based SMOTE word2vec: Neural word embeddings (CBOW/Skip-gram) XLNet: Generalized autoregressive pretraining for language understanding References Abdi H, Williams LJ

Neter, J

For youth with more severe obesity or obesity-related medical complications, medications such as GLP-1 and, in appropriate cases, MBS should be considered as additional evidence-based treatment options, not as replacements for healthy behaviors, but as tools that help young people achieve better health outcomes. Ultimately, Messiah argues that success should not be measured solely by the number on the scale, but should also consider improvements in blood pressure, diabetes risk, liver health, physical function, mental well-being, and quality of life