Bibliographic record
Abstract
Detection of somatic mutations from late stage solid tumors is a critical part of cancer treatment. Although tumor content is used as a convenient parameter to measure efficacy of detection, it fails to include two basic factors: the lower limit of detection (LLOD), and the ratio of the mutant and wild type allele frequencies. Recently, the detection of somatic mutations has expanded to liquid biopsy, early stages of cancer and population screening, which all generally carry lower copy numbers of somatic mutations compared to late stage tumors. With the growing importance of these mutations for targeted chemotherapy and other clinical applications, there is a need re-evaluate the efficacy of detection of somatic mutations. Hence, a new algorithm, Detection Index (DI), is proposed to standardize the efficacy of all molecular methods and is applicable to all types of clinical samples. Detection Index (DI) is based on two basic determinants: lower limit of detection of the mutant allele, and the ratio of the copies of the mutant allele to that of the wild-type. The benefits of DI include (a) standardization of methods detecting somatic mutations so that laboratory reports will have a uniform interpretation related to clinical picture, and (b) the flexibility to use appropriate amounts of DNA and assay conditions to achieve desired DI.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".