Mathematization of risk and benefit for first-line treatment of metastatic colorectal cancer: A graphical decision aid for patients and physicians
Bibliographic record
Abstract
3627 Background: Advances in the treatment of metastatic colorectal cancer (mCRC) have improved median overall survival (mOS) from 1yr with 5-fluorouracil (FU) therapy to >21 mos. This may come at cost of more toxicity. As the number of tested regimens increases, the question arises how to best present palliative treatment options. We present a simple way to compare treatment options in terms of risks & benefits. Methods: The literature was surveyed for reports of 1st-line systemic therapies for mCRC. The largest recent reports with detailed toxicity data were selected as representative for a regimen; if several comparable reports existed then all were selected. Toxicity sum (TS) of a regimen was calculated as % occurences in the study cohort of severe (≥ gr. 3) adverse effects: diarrhea+mucositis+neurocutaneous (excl. alopecia)+vomiting (or vomiting/nausea if not reported separately or nausea if no vomiting rep.)+febrile neutropenia (FN)(or infection if FN not rep. or leukopenia if infection not rep.)+toxic deathrate. Benefits (OS & progression-free survival PFS in months) were divided by TS to give a benefit/toxicity (B/T) ratio & plotted as benefit vs TS. Results: 30 regimens were found.OS, PFS & TS for the various regimens ranged from 8.9–24.7 and 4.9–9.2 mos, & 12–63 respectively. B/T ratios ranged from 0.1–0.53 (PFS) & 0.22–1.19 (OS); higher values more favorable. Regimens with similar B/T ratios may still differ - longer OS & PFS but at cost of more toxicity. Graphical presentation makes this clear. Intriguing & open to interpretation was a narrow range of PFS while OS varied greatly between regimens. Weaknesses of our study incl omission of some regimen-specific toxicities, & of symptom control benefit. Conclusions: Our comparative tool helps physicians discuss the large number of available options & their clinical differences easily with patients, improving informed consent & disclosure, in order to arrive at the treatment plan most appropriate to the individual. [Table: see text] [Table: see text]
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".