Generating Coherent Extracts of Single Documents Using Latent Semantic Analysis
Bibliographic record
Abstract
The insulin-like growth factor 1 receptor (IGF1R) pathway plays an important role in the pathogenesis of non-small cell lung cancer (NSCLC) and also provides a mechanism of resistance to targeted therapies. IGF1R is therefore an ideal therapeutic target and several inhibitors have entered clinical trials. However, thus far the response to these inhibitors has been poor, highlighting the importance of predictive biomarkers to identify patient cohorts who will benefit from these targeted agents. It is well-documented that mutations and/or deletions in the epidermal growth factor receptor (<i>EGFR</i>) tyrosine kinase (TK) domain predict sensitivity of NSCLC patients to EGFR TK inhibitors. Single-nucleotide polymorphisms (SNPs) in the IGF pathway have been associated with disease, including breast and prostate cancer. The aim of the present study was to elucidate whether the <i>IGF1R</i> TK domain harbours SNPs, somatic mutations or deletions in NSCLC patients and correlates the mutation status to patient clinicopathological data and prognosis. Initially 100 NSCLC patients were screened for mutations/deletions in the <i>IGF1R</i> TK domain (exons 16-21) by sequencing analysis. Following the identification of SNP rs2229765, a further 98 NSCLC patients and 866 healthy disease-free control patients were genotyped using an SNP assay. The synonymous SNP (rs2229765) was the only aberrant base change identified in the <i>IGF1R</i> TK domain of 100 NSCLC patients initially analysed. SNP rs2229765 was detected in exon 16 and was found to have no significant association between IGF1R expression and survival. The GA genotype was identified in 53.5 and 49.4% of NSCLC patients and control individuals, respectively. No significant difference was found in the genotype (P=0.5487) or allele (P=0.9082) frequencies between the case and control group. The present findings indicate that in contrast to the <i>EGFR</i> TK domain, the <i>IGF1R</i> TK domain is not frequently mutated in NSCLC patients. The synonymous SNP (rs2229765) had no significant association between IGF1R expression and survival in the cohort of NSCLC patients.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".