Optimal frequency of scans for patients on cancer therapies: A population kinetics assessment
Bibliographic record
Abstract
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 We used the Excel formula x = EXP(‐ t n *0.693/ t 1/2 ) to calculate proportion of residual patients remaining progression‐free at different times, where t n is the interval in weeks between scans (eg, 6 weeks), * indicates multiplication, 0.693 is the natural logarithm of 2, and t 1/2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".