Nomograms to Predict Serious Adverse Events in Phase II Clinical Trials of Molecularly Targeted Agents
Bibliographic record
Abstract
PURPOSE: A tool that quantifies the risk of treatment-related toxicity based on individual patient characteristics can augment the informed consent process and safety monitoring in the setting of phase II cancer treatment trials of molecularly targeted agents (MTAs). METHODS: A regression model was constructed to predict the risk of a serious adverse event (SAE) with an MTA and presented as a nomogram. Estimation of risk can be performed by integrating risk estimates from the nomogram and from a reference or average patient. Internal validation was performed using bootstrapping techniques. RESULTS: A total of 578 patients were treated with one of 14 MTAs given alone or in combination on one of 27 clinical trials performed by the Princess Margaret Hospital Drug Development Program between 2001 and 2006. Approximately 50% and 24% of patients experienced an SAE and an attributable SAE (SAEatt) during cycle 1, respectively. Albumin, lactate dehydrogenase (LDH), number of target lesions, prior radiotherapy, Charlson score, age, and performance status were included in the optimal model as predictors of a cycle 1 SAE, whereas the number of prior chemotherapy regimens, baseline creatinine, LDH, prior radiotherapy, Charlson score, body-surface area, and performance status were included as predictors of an SAEatt. Moderate-good internal validity was demonstrated, with area under the curve estimates ranging from 56.7% to 86.1% for all SAEs and 63.0% to 89.7% for SAEatts. CONCLUSION: A regression model was constructed that predicts the SAE and SAEatt risk for an individual patient during cycle 1 of phase II trial treatment with moderate to good internal validity. External validation is still required.
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 | 0.046 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".