Supporting Cases for the Depletion Model of Immune Check pointInhibitor Therapy in Cancer
Bibliographic record
Abstract
Immune checkpoint inhibitor therapy has been introduced into cancer clinics for over a decade with big hopes and hypes, yet the true mechanism behind this therapy is still unclear in many ways. In a previous article we have introduced a new working model for this therapy based on the partial depletion of PD1 positive T cells. This model, as we called it the depletion model, explains all clinical observations including the trigger effect and the hyper-progression associated with anti-PD1/PDL1 antibody use. One critical prediction from this model is that under repeated dosing of anti-PD1 antibody, any antitumor response must be mediated by PD1-negative T cells, because that all PD1-positive T cells are removed by the antibody. Unless this prediction can be confirmed, the depletion model will not be supported by evidence. In this report, using few real-world cases, we provide supporting evidence to support the various aspects of the depletion model.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".