Investigating Polypharmacy for Patients with Multi Encounters Using the QL4POMR Framework
Bibliographic record
Abstract
Comorbidity is the presence of two or more clinical conditions encountering a patient at the time of visit, hospitalization or being with the patient for a considerable time. Most of these conditions are chronic conditions. Treatment of comorbidity and patients with comorbid diseases poses a major health issue for millions of people worldwide and requires collaboration between clinical providers as well as the use of knowledge cross domains of practice. Having multiple diseases inevitably lead to the use of multiple drugs, a condition known as polypharmacy. This article investigates internists might how to tackle polypharmacy using our previously developed QL4POMR which is kind of medical translation system. QL4POMR provide clinicians with SOAP to describe the patient case at the bedside and link it to the biomedical repositories like the OpenTargets and DrugBank. Based on this link clinician can identify issues associated with polypharmacy like disease to drug interactions or drug to drug interactions. Polypharmacy for comorbid conditions like asthma and hypertension has been investigated as proof of concept.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
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".