Plasma osteopontin and the prognosis of pleural mesothelioma.
Bibliographic record
Abstract
11109 Background: Cytoreductive surgery for malignant pleural mesothelioma (MPM) should be reserved for patients with favorable tumor biology. Osteopontin (OPN) and the ratio of absolute neutrophil to absolute lymphocyte counts (NLR) have been reported as possible prognostic biomarkers. These were studied with other clinical/ laboratory variables in a mixed surgical/non-surgical MPM population to define independent predictors of survival (OS) and progression (TTP). Methods: Forty-four MPM patients (12 F, 32M; 26 cytoreduction, 18 no cytoreduction; 31 epithelial, 13 non-epithelial; 15 Stage I/II, 29 Stage III/IV) were examined with regard to pretreatment plasma OPN (ELISA, R&D, Minneapolis, MN), NLR age, gender, therapy, histology, stage, platelet count and WBC count. Cut points for age, OPN, NLR, platelets, and WBC were determined by X-tile Software (Yale, New Haven, CT) and univariate/multivariate Cox analyses performed. Results: Median OS were 11 m, 21m, and 8m for all 44 MPMs, cytoreduced and non-cytoreduced MPMs, respectively. Of platelet count, WBC, NLR, and OPN, only OPN was statistically significant between Stage I/II and Stage III/IV (80.3 ng/ml vs 148 ng/ml, p<0.018). The only independent covariate predictive of OS was plasma OPN. For TTP in cytoreduced patients, only age, stage, platelet count, and OPN were significant in univariate analysis, and multivariate modeling retained stage (p=0.04, HR=2.75, 95% CI=1.0517 to 7.1879) and OPN (p=0.0008, HR=17.471, 95% CI=3.3054 to 92.3461). Conclusions: Plasma OPN is promising for the stratification of tumors into good or bad risk categories and to help select potential candidates for cytoreduction and further postoperative therapy. [Table: see text]
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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".