Epidermal growth factor receptor immunoexpression evaluation in malignant salivary gland tumours
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine epidermal growth factor receptor (EGFR) expression in malignant salivary gland tumours and its possible relationships with clinical and morphological findings, disease course and prognosis. PATIENTS AND METHODS: The study sample comprised 88 patients diagnosed and treated for primary malignant salivary gland tumours between January 1992 and December 2002. We analysed EGFR expression using immunohistochemistry on formalin-fixed, paraffin embedded surgical specimens of all patients. Statistical analysis was used to investigate possible relationships between EGFR expression and clinical findings, histological findings, disease course and patients survival. RESULTS: Of all cases, 32 (36.4%) were EGFR positive. There was a statistically significant correlation between EGFR expression and histological grade. No other variable was correlated with EGFR expression including the overall and disease-free survival. Stage classification was the only parameter in multivariate analysis that was an independent predictor of low overall and disease-free survival. CONCLUSION: EGFR is not a useful indicator of prognosis in malignant salivary gland tumours. However, the EGFR expression in salivary gland cancers like adenocarcinomas, undifferentiated carcinomas, mucoepidermoid carcinomas or salivary duct carcinomas suggests that these tumours may be a candidate for therapy investigation directed at EGFR.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".