<i>EGFR</i> gene status in cytological samples of nonsmall cell lung carcinoma
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
BACKGROUND: In nonsmall cell lung cancer (NSCLC), the development and clinical application of tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR) has required the investigation of EGFR status by gene copy number and/or mutation analysis. This review aimed to present the current knowledge of the use of cytological specimens for EGFR testing in lung cancer. METHODS: A systematic computerized search was performed of the MEDLINE(R) and EMBASE databases to identify articles reporting the use of cytological samples for determining EGFR status in NSCLC. RESULTS: Data were extracted from 30 original articles. An additional 19 reviews, consensus statements, and editorials were selected from 175 retrieved papers. Different techniques using cell blocks, scraped cells from archival slides, and fresh cells have shown promising results and include fluorescent in situ hybridization (FISH), direct sequencing, and quantitative polymerase chain reaction (PCR), with similar or higher accuracy and sensitivity than surgical specimens. Preservation and quality of the extracted DNA seem to matter more than the actual number of tumor cells present in the samples. However, major issues still reside in the amount of material, the interference from background non-neoplastic cells, and standardization of parameters for cytological samples. CONCLUSIONS: This analysis provided evidence that cytological material is suitable for detecting EGFR status using several different methodologies and preparations. New prospective, clinical studies are encouraged for collection and handling of cytological samples as well as for validation of novel techniques in large cohorts.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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