Understanding and Assessing Potential Serious Adverse Events: A Practical Approach to Understanding the Benefits and Harm of Psoriasis Treatments
Bibliographic record
Abstract
BACKGROUND: Any therapeutic intervention carries with it the potential for benefit and harm. Generally, benefit is far more common than risk; however, risk aversion drives many of the treatment decisions made by patients and their physicians. OBJECTIVE: To provide guidelines to help clinicians improve their understanding of causality and the interpretation of harm. METHODS: A group of dermatologists involved in data safety monitoring boards, clinical trial investigators, and a clinical epidemiologist identified the need for practical advice on how to understand and explain causality and harm and combined to share their knowledge. RESULTS: An explanation of how data are collected and the environment that shapes the data seen by clinicians is presented. The article spans an overview of the regulatory environment that informs trial design for regulatory approval to a description of types of designs that inform safety and techniques, such as the rule of three, to provide guidance to clinicians in interpreting the data. CONCLUSION: Communicating the potential for harm to patients is critical. Placing the potential for rare and serious risks into perspective for the patient is as important as discussing the potential benefits of medication.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".