A tool for assessing adverse events in phase I/II oncology clinical trials
Bibliographic record
Abstract
6518 Background: RECIST and NCI's Common Terminology Criteria are accepted systems that have standardized the reporting of oncology clinical trial outcomes. A standard system for attributing causality to Serious Adverse Events (SAEs) is lacking which can impact drug development and patient safety. The objectives of this study were to: 1) understand the clinical reasoning behind causality assessment during phase I/II oncology clinical trials; and, 2) use this information to develop a causality assessment tool for oncology. Methods: In-depth interviews were conducted with oncologists and trial coordinators at 6 Canadian academic cancer centres. Five main conceptual categories were explored: clinical reasoning; information resources; tools; challenges and concerns; and education. Interviews were recorded and transcribed verbatim. Individual interview content analysis was followed by thematic analysis across the interview set. A new causality assessment tool was developed based upon the qualitative findings and an analysis of existing generic tools. Results: Thirty-two interviews were conducted between May and August 2006 (65% participation). Half of participants were female, 66% were oncologists and 42% had more than 10 years of clinical trial experience. Data showed that participants use a common strategy to assess causality: they gather information, eliminate alternative explanations, and consider the study drug as the cause of the SAE. Over half cited the quality of information resources as a major factor contributing to uncertainty when assessing causality. Participants expressed the need for a standardized approach to causality assessment in oncology clinical trials. The tool developed in this study guides users to consider 5 statements related to potential alternative etiologies and 4 related to other factors that support a drug-SAE connection. The user is asked for their overall impression using a continuous probability rating scale. Conclusions: Attributing causality to SAEs is complex and uncertain. Clinicians describe using a logical system of reasoning, but have encountered barriers which must be addressed. We have developed and are validating a new tool to assist cancer clinicians in providing higher quality safety data about new cancer drugs early in development. [Table: see text]
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 | 0.175 | 0.379 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".