Balancing Efficacy and Tolerability: Assessing Treatment Impact and Side Effects in Prescribing Decisions
Bibliographic record
Abstract
In the realm of medical practice, prescribing decisions are multifaceted, often requiring a delicate balance between therapeutic benefits and potential adverse reactions. This abstract delves into the intricate interaction between these determining factors, elucidating their impact in clinical settings. Healthcare providers face the continuous challenge of selecting treatments that offer optimal therapeutic benefits while minimizing the risk of adverse reactions. This task necessitates a comprehensive evaluation of available medications, considering their efficacy profiles and potential side effects. Understanding the nuances of these determinants is crucial for ensuring patient safety and treatment success. This study explores the dynamics of prescribing decisions through the lens of situational influences and side effects. By examining real-world data and impartial observations, we aim to clarify the decision-making process of healthcare professionals. Additionally, we consider the role of drug surveillance methods in monitoring and mitigating risks associated with medication use. Overall, this research aims to shed light on the complexities of prescribing decisions and provide insights into strategies for optimizing patient care while minimizing adverse outcomes.
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 | 0.041 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".