An evaluation of the Index4 tool for chemotherapy toxicity prediction in cancer patients older than 70 years old
Bibliographic record
Abstract
Chemotherapy, although beneficial for improving outcomes in both localized and metastatic cancers, may be associated with significant adverse effects, especially for patients with decreased functional reserves. Prediction of patients who will not tolerate well chemotherapy treatment may help in modifying treatment plans and in reallocating resources to vulnerable patients. One hundred seventeen consecutive cancer patients over the age of 70 scheduled for chemotherapy treatment in a single cancer center were included in the study. Prediction of adverse chemotherapy outcomes were calculated using a prediction tool proposed and validated from the Cancer and Aging Research Group (CARG) and a prediction tool proposed by us, called Index4. The 2 tools were compared for their ability to predict grade 3 and 4 toxicities, Emergency Department (ED) and hospital admissions and chemotherapy discontinuation. The accuracy of both predictive tools was suboptimal. A high CARG score had a sensitivity of 46.3% and a specificity of 82% and an Index4 of 1 or above had a sensitivity of 53.7% and a specificity of 60% in predicting grade 3-4 adverse effects. The performance of the 2 tools in predicting ED and hospital admissions and chemotherapy discontinuation was comparable. An Index4 score of 0 was superior in predicting absence of grade 3-4 toxicities than a low CARG score (p = 0.002, McNemar's test). The CARG tool for chemotherapy adverse effect prediction in geriatric cancer patients and the Index4 were able to predict adverse outcomes with moderate accuracy. Given its ease of calculation Index4 may be an alternative to CARG tool, suitable for a busy oncology practice.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".