What Comes First to Your Mind: Drug Efficacy or Drug Safety?
Bibliographic record
Abstract
Disease-treatment guidelines are most often implemented clinically as part of comprehensive medication management to ensure patients receive the most effective and evidence-based care. Post-marketing pharmacovigilance programs are the main source of medication safety monitoring. However, pharmacovigilance programs have multiple problems including underreporting of adverse drug events (ADEs), inconsistent reporting standards, and difficulties in data analysis and interpretation making it difficult to examine safety information. Patients with multiple chronic diseases leading to polypharmacy are at an increased risk of ADEs. There are various elements that constitute a successful approach while balancing efficacy and safety including: 1) Medication-related problems and ADE risk identification, prevention, and management; 2) Interdisciplinary collaboration; 3) Beneficiary-centered personalized care; 4) Technology integration; 5) Outcome evaluation and quality improvement; and 6) Pre-emptive pharmacovigilance studies by the virtual addition of drugs to patients’ drug regimen. In conclusion, medications generally enter the market with proven efficacy but often lack short- and long-term safety information. A personalized transformative approach leveraging innovative clinical science-based technology and medication safety experience can create a more effective, patient-centered approach to managing complex medication regimens, ultimately improving patient safety and outcomes.
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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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".