Multielemental Analysis of Pleural Effusion to Determine the Relationship Between Air Pollution and Lung Cancer
Bibliographic record
Abstract
Pleural effusion (PEf) is a pathophysiological condition that is associated with the accumulation of pleural fluid (Pfl), presenting itself in diseases such as:pneumonia Obstructive Pulmonary Disease (COPD), pulmonary fibrosis, lung cancer and also due to cardiac conditions.The elemental composition of the PEf can serve as a biomarker of the pathophysiological state of comorbidities PEf samples (N=129) were collected of the Metropolitan Area of the Valley of Mexico in the emergency room service of the National Institute of Respiratory Diseases (INER), during 2021-2022.The elemental composition of samples were analyzed by ICP-OES and ICP-MS, determining sixteen elements.Among the most abundant were essential elements such as: Fe median 1313.7 p5-p95 (201.5-4087ppb),Zn 381.0 (52.6-821ppb) ,Cr 23.3 (8.1-36.2ppb), in addition to elements of anthropogenic origin in high concentrations such as Al 317.32 (104.5-505ppb),Ti 26.41 (11.2-61.9ppb),Sr 21.54 (10.3-41.9ppb),Sn 0.20 (0.02-1.3ppb ).The presence and probably the differences among trace elements, metals and metalloids concentrations in PE could be associated with its essential function in the human body but the presence and high concentrations of someone could associated with anthropogenic activities from urban particles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".