microRNA evaluation of unknown primary lesions in the head and neck
Bibliographic record
Abstract
Unknown primary malignancy in the head and neck is not an infrequent diagnosis for patients with metastatic cervical lymph nodes. Although linked with a relatively good prognosis following radiation treatment, widespread radiation is coupled with significant morbidity. Altered microRNA (miRNA) expression has been associated with both cancer progression and metastasis. We sought to determine whether miRNA expression analysis could be used as a diagnostic tool to discover the primary site of malignancy, within the head and neck. We used quantitative real-time PCR to identify miRNA expression profiles of squamous cell carcinoma of the tonsil, base of tongue and post-nasal space, as well as their corresponding metastatic lymph nodes, from 6 patients. Our results revealed that each cancer maintained its expression profile between the primary site and the nodal metastasis (r = 0.82, p < 0.0001). In addition, each anatomical sub-site maintained a distinct miRNA profile between individual patients (r = 0.79, p < 0.0001). Finally, between sub-sites, the miRNA profiles were distinct (p < 0.0001). As proof of principle, our study provides an indication that miRNA expression analysis may be useful to compare the primary lesion and local metastatic disease. This may be clinically relevant to predict the primary site of origin of metastatic disease, when the primary site remains obscure.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".