Biomarkers in wound drainage fluids of head and neck squamous cell carcinoma patients receiving neck dissection: A pilot study
Bibliographic record
Abstract
AIM: In a pilot prospective study, we aimed to test the feasibility and report on the preliminary results on the expression of molecular biomarkers in wound drainage fluids (WDFs) of operated head and neck squamous cell carcinoma (HNSCC) patients. MATERIAL AND METHODS: Nineteen patients undergoing primary tumor resection with en-block neck dissection were enrolled. In postoperative days 1-3, the expression of several biomarkers in WDFs was measured using enzyme-linked immunosorbent assay (ELISA) kits and correlated with clinical and histopathologic features. RESULTS: The expression of stromal cell-derived factor 1 (CXCL-12) was significantly increased in WDFs in presence of lymph node metastases, extranodal extension (ENE), and in case of close resection margins. In addition, Osteopontin expression was significantly increased in presence of ENE, whereas transforming growth factor beta (TGF-β) detection was significantly reduced. At multivariate analysis, CXCL-2 levels in both day 1 and 3 post-surgery were the only factor which retained significance in the prediction of close surgical margins (p = 0.028 and 0.025 for day 1 and day 3, respectively). Both CXCL-2 and Ostepontin assays were significantly correlated with ENE (p = 0.018 and 0.035 for day 1; 0.052 and 0.025 for day 3, respectively) whereas TGF- β expression was significant at day 1 only (p = 0.038). CONCLUSIONS: Our pilot study showed that WDFs could qualify as a potential source of relevant postoperative information. Further studies are needed to confirm the prognostic impact of CXCL-12, Osteopontin and TGF-β expressed in WDFs on the personalized management of HNSCC.
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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 | 0.001 | 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".