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Comparison Between the 3D-CBS Screening and the Liquid Bio and Other Screening of Cancer Procedures

2024· article· en· W4402834257 on OpenAlexaboutno aff
D. Crosetto

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsCancerComputer scienceCancer detectionMedicineInternal medicine

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.026
GPT teacher head0.362
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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