Bibliographic record
Abstract
Does gene testing indicate a switch from an histopathological to a molecular approach of human diseases ? Disease management in oncology is already improved by gene testing, at least for some specific cancers. It is however necessary to distinguish the analysis of genes specific to the tumour which gives clues about the fate of the tumours, from those unique to the patients, which gives clues about the future of the person. For the latter so-called germline mutations, wide scale gene-default screening would put pressure on resource allocation from the health care systems of developed countries. Currently the cost of detecting of 700 genes in the whole French population would exceed the whole health budget of the country for the next 10 years. Even if we can anticipate a dramatic decrease in the unit cost of these genetic tests in the future, their diffusion should not be controlled exclusively by technological and market forces. In this paper, we propose to discuss four main parameters for regulating these genetic tests, using as an archetypal example their application to cancer prevention and treatment: (1) which specific cancer disease is targeted by the test (prevalence, incidence, likelihood of cure with current therapeutics, number of years of life potentially saved...); (2) what are the characteristics of the genes tested and which level of evidence is required about the predictive value of the test; (3) what are the size and characteristics of the population who will be offered the test, and (4) which process and public control are necessary before market approval of the test and reimbursement of related expenditures by health care insurance schemes.
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 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 | 0.038 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".