Comorbidities, concominant medications, and clinical guidelines/algorithms in treating elderley patients with hormone receptor positive early breast cancer
Bibliographic record
Abstract
e20680 Background: Guidelines and algorithms have been published for aspects of BC care such as adjuvant hormonal therapy, f/up testing, and mgmt of hormonal S/Es. This study examined how CPG's for BC address comorbidity in older patients, and applicability to frail elderly. Patterns on the use of adj hormonal therapy in patients > 60, number of comorbidities, and concurrent medications was assessed at a single institution. Methods: Guidelines were obtained by searching the National Guideline Clearinghouse, as well as Pubmed. Guidelines on adjuvant anti-estrogen use for women with BC, follow-up, the use of bisphosphonates, and management of arthralgia in endocrine therapy treated patients were assessed. For the chart review, 290 consecutive patients over the age of sixty were reviewed for medication use, comorbid medical conditions, and anti-estrogen use. Results: The examined clinical guidelines and algorithms did not discuss the applicability of recommendations for older patients or those with multiple comorbidities. Fifty percent of patients in our sample had conditions where NSAID/COX2 use would be contraindicated. The median number of concurrent medicines at initial consult was 4. Patients >70 were significantly more likely to not receive adjuvant hormonal therapy (21% vs. 10%, p=0.025). Conclusions: CPG's do not address the applicability of treatments in patients with multiple comorbidities or the frail elderly. Older patients may be significantly less likely to receive adjuvant hormonal therapies. A significant proportion of patients in our sample had risks for SAE's from the use of NSAIDs, which are commonly recommended to manage arthralgias associated with hormonal therapies. Assessing commonly used hormonal treatment and side-effect management drugs for potential interactions with concurrent medications is an important clinical step in personalized therapy. No significant financial relationships to disclose.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".