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EFFECT OF COEXISTING DISEASES ON THE TREATMENT OF UNRELATED DISEASE NEEDS MORE STUDIES

2006· letter· en· W1488906223 on OpenAlexaboutno aff
Huai Yong Cheng

Bibliographic record

VenueJournal of the American Geriatrics Society · 2006
Typeletter
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiseaseLife expectancyAdverse effectIntensive care medicineDepression (economics)Diabetes mellitusInternal medicine

Abstract

fetched live from OpenAlex

To the Editor: Older people often have multiple chronic diseases1 and take many medications.2,3 Multiple coexisting diseases and multiple medications are associated with more adverse effects2 and poor adherence to taking medications.3 Nevertheless, how one disease affects treatment of another unrelated disease has rarely been studied. Using Medical Expenditure Panel Surveys, Dr. Harman et al. have shown that hypertension and diabetes mellitus, but not heart disease or arthritis, were associated with a greater likelihood of receiving adequate depression care in older people.4 The reason could be due to frequent contacts with primary care providers.4 In contrast to their findings, Dr. Redelmeier et al., using the Ontario Drug Benefit program data, have shown that elderly patients with one disease are undertreated for another unrelated disease.5 For example, a patient with pulmonary emphysema received less treatment for lipid-lowering agents than a patient without pulmonary emphysema, and patients with psychotic syndromes were consistently unlikely to receive lipid-lowering or medical arthritis treatments, but patients with breast cancer were just as likely to receive glaucoma treatment as patients without breast cancer.5 The free medications for older people in the drug program indicated that cost was not a factor. Several alternative explanations were explored.5 First, chronic diseases associated with shorter life expectancy make long-term preventive therapy, such as taking lipid-lowing agents, unwanted. Second, adding more medications increases the risk of potential drug interaction and adverse events. Third, time constraints, communication problems, patient's preferences, and priorities of the specialist may limit time to address more than one disease effectively. Fourth, elderly patients with chronic disease may be exhausted and reluctant to accept multiple interventions. Last, it is often sensible to postpone minor treatment until major diseases are resolved. It was recently found that adhering to disease-based clinical practice guidelines in older people with multiple coexisting diseases may have undesirable effects, including adverse drug effects, cost burden, and multiple medications.6 Disease-based clinical practice guidelines might not work well for elderly patients with multiple coexisting diseases.6,7 Some healthcare providers feel that a typical trial patient is not necessarily the typical patient in their practice and question the applicability of the guidelines based on the trials.8 Therefore, healthcare providers might not be willing to follow the guidelines that were based on the trials.8 This could be another factor for undertreatment of one or more diseases. The true answer for almost opposite findings from these two groups4,5 is still unknown. Alternatively, drug-related morbidity and mortality was estimated to cost $76.6 billion in the ambulatory setting alone in the United States.9 Undertreatment of one disease because of one unrelated or multiple coexisting diseases might not be a bad thing. We have to ask ourselves how many medications are enough for older people with multiple coexisting diseases.10 Studying the influence of one chronic condition on the treatment of another chronic condition in older people4,5 is important and complex. More research is warranted. I have no conflicts of interest and no sponsors and am the sole author of this letter.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.077
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0110.002

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.

Opus teacher head0.029
GPT teacher head0.337
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations2
Published2006
Admission routes1
Has abstractyes

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