Relationship of Thyroid Transcription Factor 1 to EGFR Status in Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Activating mutations of the epidermal growth factor receptor (EGFR) gene are known to drive a proportion of non-small-cell lung cancers. Identification of lung cancers harbouring such mutations can lead to effective treatment using one of the agents that targets and blocks egfr-mediated signalling. METHODS: All specimens received at the BC Cancer Agency (Vancouver) for EGFR testing were prospectively identified and catalogued, together with clinical information and EGFR status, over a 14-month period. RESULTS: Specimens from 586 patients were received for EGFR testing, and EGFR status was reported for 509 patients. No relationship between specimen type or site of origin and EGFR test failure rate was identified. Concurrent immunohistochemical (ihc) status for thyroid transcription factor 1 (ttf1) was available for 309 patients. The negative predictive value of ttf1-negative status by ihc was 94.2% for predicting negative EGFR status. CONCLUSIONS: In patients with limited tissue available for testing, a surrogate for EGFR status would aid in timely management. Immunohistochemistry for ttf1 is readily available and correlates highly with EGFR status. In conjunction with genetic assays, ttf1 could be used to optimize an EGFR testing strategy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".