Validazione di nuovi marcatori di neoplasia: La posizione ufficiale dell'"European Group on Tumor Markers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biomarkers are playing increasingly important roles in the detection and management of patients with cancer. Despite an enormous number of publications on cancer biomarkers, few of these biomarkers are in widespread clinical use. In this review, we discuss the key steps in advancing a newly discovered cancer candidate biomarker from pilot studies to clinical application. Four main steps are necessary for a biomarker to reach the clinic: Analytical validation of the biomarker assay, clinical validation of the biomarker test, demonstration of clinical value from performance of the biomarker test, and regulatory approval. In addition to these 4 steps, all biomarker studies should be reported in a detailed and transparent manner, using previously published checklists and guidelines. Finally, all biomarker studies relating to demonstration of clinical value should be registered before initiation of the study. Application of the methodology outlined above should result in a more efficient and effective approach to the development of cancer biomarkers as well as the reporting of cancer biomarker studies. With rigorous application, all stakeholders, and especially patients, would be expected to benefit.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it