Bibliographic record
Abstract
We examined the possibility that the anti-estrogens, tamoxifen (TX) and toremifen (TO) interacted with the immune system. Indeed, both TX and TO stimulated cells mediated cytotoxicity reactions by various killer cells: killer T (TK), natural killer (NK), lymphokine activated killer (LAK) cells. Both TX and TO inhibited the growth of tumors that express estrogen receptors. Thus these antiestrogens inhibited tumor growth and stimulated killer cells for cytotoxicty on such tumors. Therefore these agents were presumed to stimulate tumor immunity. We tested the P815 mouse mastcytoma with TK, LK, and TX or TO. A therapeutic effect was observed in both experiments. The SL2-5 murine lymphoma was tested with NK and TX cells or TO cells and successful immunotherapy was observed. We digested human breast carcinomas and lung tumors with collagenase. The killer cells were separated from tumor cells on Ficoll gradients. TX and TO enhanced the cytotoxic effect of autologous killer cells on the corresponding tumor cells. This experiment indicates that the results obtained in animals are also valid for human malignant disease.
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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 | 0.005 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".