Optimal frequency of scans for patients on cancer therapies: A population kinetics assessment
Bibliographic record
Abstract
BACKGROUND: Optimal frequency of follow-up scans for patients receiving systemic therapies is poorly defined. Progression-free survival (PFS) generally follows first-order kinetics. We used exponential decay nonlinear regression analysis to calculate half-lives for 887 published PFS curves. METHOD: is the PFS half-life in weeks. RESULTS: Proportion of residual patients predicted to remain progression-free at each subsequent scan varied with scan intervals and regimen PFS half-life. For example, with a 4-month half-life (17.3 weeks) and scans every 6 weeks, 21% of patients would progress by the first scan, 21% of the remaining patients would progress by the second scan at 12 weeks, etc With 2, 6- and 12-month half-lives (for example), the proportion of remaining patients progressing at each subsequent scan if repeated every 3 weeks would be 21%, 8% and 4%, respectively, while with scans every 12 weeks it would be 62%, 27% and 15%, respectively. Furthermore, optimal scan frequency can be calculated for populations comprised of distinct rapidly and slowly progressing subpopulations, as well as with convex curves arising from treatment breaks, where optimal scan frequency may differ during therapy administration vs during more rapid progression after therapy interruption. CONCLUSIONS: A population kinetics approach permits a regimen- and tumor-specific determination of optimal scan frequency for patients on systemic therapies.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".