Bibliographic record
Abstract
OBJECTIVE: Menopause occurs at a critical juncture in life when preventative health care can have a major impact. However, recommendations for immunizations are often neglected, leading to unnecessary morbidity and mortality in aging women. The aim of this review is to highlight the importance of immunization checkpoints at menopause to optimize the quality of care and health maintenance in older women and to provide an overview of the impact of immunizations on women's health. METHODS: This is an opinion article based on the current US and Canadian guidelines. A review of various guidelines from the Centers for Disease Control and Prevention and National Advisory Committee on Immunizations were conducted for each vaccine. RESULTS AND CONCLUSIONS: Disease prevention benefits are well established for several diseases, such as hepatitis A, hepatitis B, tetanus, human papillomavirus, streptococcus pneumonia, shingles, and COVID-19. During clinical encounters, a needs assessment regarding vaccinations should be conducted. However, barriers to adult vaccination including lack of patient and provider knowledge about the need for vaccination, lack of priority for preventive services, and concerns regarding costs, insurance coverage, and reimbursement all contribute to the adult immunization gap. Given the importance of immunization and the need to decrease vaccine-preventable diseases, it is the obligation of healthcare practitioners to recommend vaccines and provide education on vaccination guidelines and associated risks. As women often seek medical attention at menopause because of changes in their physiology that require attention, it is the ideal time to discuss the importance of immunization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".