Prognostic significance of elevated serum <scp>CD</scp>44 levels in patients with oral squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Availability of reliable methods distinguishing high-risk recurrent tumours from regressive tumours prior to surgery could help in better management of the disease. This study was aimed to estimate pre-surgical serum CD44 concentration and assess the possibility of using it as a non-invasive prognostic tool in oral cancer. METHODS: ELISA was performed on pre-surgical serum samples from 64 primary oral cancer patients and 16 healthy individuals to estimate soluble CD44 levels. Immunohistochemistry was performed on parallel 64 solid tumours and 10 recurrent tumours. All patients clinically followed up for median period of 19.2 months and obtained prognostic information correlated with CD44 concentration in serum as well as in tumours. RESULTS: Serum CD44 concentration was found significantly high in patients as compared to healthy individuals (P < .001) and also in patients whose disease locally recurred as compared to those did not recur (P = 0026). High serum CD44 concentration inversely affected on patients survival (P = .032). CD44v6 staining intensity was detected significantly high in recurrent tumours as compared to primary tumours (P < .001), and it also correlated with poor survival (P < .001). Furthermore, in combination, patients with increased CD44 concentration in serum and CD44v6 expression in tumours significantly correlated with local recurrence (P < .001) and poor survival (P < .001). CONCLUSION: Our data suggest that the ELISA-based estimation of pre-surgical serum CD44 concentration could be a non-invasive reliable method distinguishing high-risk recurrent tumours which can further assist in post-surgery treatment planning.
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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) | 0.002 | 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".