Relationship between Pretreatment Serum Albumin Levels with the Risk of Malignant Pleural Mesothelioma
Bibliographic record
Abstract
Background: Malignant Pleural Mesothelioma (MPM) is a very rare and aggressive form of cancer. Recently it was found that pretreatment Serum Albumin (SA), the main circulating protein in blood is a significant prognostic factor for MPM patients. The objective of this present article is to show the relationship between pretreatment Serum albumin (SA) levels with the risk of MPM. Methods: Generalized additive model (GAM), an advanced regression analysis method has been introduced here to find this mathematical relationship between the response variable (SA) and the cofactors. Results: The main determinates of SA are identified - asbestos exposure, hemoglobin, disease diagnosis status (patients having MPM) are the factors having significant association with SA, whereas duration of asbestos exposure, duration of disease symptoms, total protein (TP), Pleural lactic dehydrogenise (PLD), pleural protein (PP), pleural glucose (PG) and C-reactive protein (CRP) are the significant continuous variables for SA. The non-parametric estimation part of this model shows Lactate dehydrogenase (LDH) and Glucose level are the significant smoothing terms. Additionally it is also found that, second and third order interactions between cofactors are highly significant for SA. Conclusions: The findings of this present work can conclude that - serum albumin may play the role of a very significant prognostic factor for MPM disease and it has been established here through mathematical modeling. Few of the findings are already been exist in MPM research literature whereas some of the findings are completely new in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".