Bibliographic record
Abstract
What determines the survival time in a cancer case? An obvious aspect is the malignancy of the tumor. Based on our clinical observation, this so-called malignancy includes three aspects: 1) The mode of tumor replication, i.e., how rapidly a tumor replicates; 2) The ability to form distant metastasis; and 3) The ability to cause symptoms, often related to the ability to drive local inflammation. But tumor malignancy alone does not seem to correlate with cancer survival. Another factor contributing equally critical or even more critical to cancer survival is often ignored. It is the status of host antitumor immunity. Tumor malignancy is only the determinant factor for cancer survival when concomitant antitumor immunity is absent in a case. With a strong antitumor immunity, regardless of tumor malignancy, a case is likely to survive much longer than that of a case with a week or none antitumor immunity. Thus, antitumor immunity is a more influential factor than tumor malignancy on cancer survival. Yet, the status of antitumor immunity has been consistently ignored in clinical management of cancer and still is as of today. Unless it is clearly demonstrated in cancer cases that the lack of antitumor immunity is the most critical factor influencing survival, clinicians are unlikely to pay attention to the status of antitumor immunity in each case, less to say to manage accordingly. There have been many previous studies to link immunity to cancer survival in numerous statistical analyses, but these findings did not raise alert in clinicians to the point that the status of antitumor immunity is being considered in selection of treatment plan when it comes to individual cases. In this report we try to describe few individual cases in which the status of antitumor immunity is clearly responsible for survival. We hope the conmen aspects and clear contrasts in these cases will bring a more vivid picture to clinicians who will then realize how important is the status of antitumor immunity in each cancer cases and learn to select treatments based on the status of that immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".