Comparison Between the 3D-CBS Screening and the Liquid Bio and Other Screening of Cancer Procedures
Bibliographic record
Abstract
This article provides the comparison between the expected results in cancer deaths and costs reduction from non-invasive cancer screening procedures. These two screening procedures are the 3D-CBS (3-D Complete Body Screening) and the Liquid Bio (Liquid Biopsy). A comparison will also be made on the efficacy of the officially approved and currently used mammography, colorectal and PAP-smear screening procedures in many countries. We should all face the same challenges against cancer, not a competition between each other, and should accept the efficacy test on a sample population in a specific location with respect to the mortality rate of the previous 20 years in the same location. The author’s 3D-CBS invention from the year 2000 can provide the features of a non-invasive, 2-minute, safe, low-cost, efficient, cancer-screening test, that can detect tumors with only 100 cancer cells that no other device can provide simultaneously. It revolutionizes medicine by recording anomalous biological processes simultaneously of the entire body, by basing the diagnosis on the trend of anomalies rather than from a single exam, by extracting patterns of pre-diseases formation, however the 3D-CBS was never funded. The Liquid Bio is a blood test aimed to detect a tumor’s mutational profile. It was announced in 2007 as a breakthrough in Time magazine. In 2012 at the WCC in Montreal was announced to provide a sensitivity greater than $94 \%$ and specificity greater than $91 \%$ in the early detection of the 4 big cancer killers, but the test was later abandoned. Liquid biopsy received conspicuous funding but did not produce results in cancer deaths reduction, although it has recently gained a lot of traction. Mammography and colorectal screening are not effective because there is no significant mortality rate difference between countries who do or don’t. PAP smear screening is effective but is a small percent of the total 10 million/year cancer deaths.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".