Procedures Performed during Hospitalizations for Malignant Pleural Effusions: Data from the 2012 National Inpatient Sample
Bibliographic record
Abstract
BACKGROUND: Malignant pleural effusions (MPE) are a common clinical problem. Little is known about the burden of MPE and of the treatments used to alleviate its symptoms on the United States Health Care System. OBJECTIVES: We aimed to obtain a better portrait of inpatient pleural procedures performed in the United States. METHODS: We conducted a retrospective analysis of MPE-associated hospitalizations using the Healthcare Cost and Utilization Project-Nationwide Inpatient Sample, Agency for Healthcare Research and Quality (HCUP-NIS 2012). Descriptive statistics were used to analyze procedures performed and their complications. Univariate and multivariate logistic regression models were used to explore the relationship between procedures performed and inpatient mortality and length of stay. RESULTS: Among the 126,825 hospital admissions with a diagnosis of MPE, 72,240 included one or more pleural procedures. Thoracentesis (54,070) was the most frequently performed procedure followed by chest tube placement (23,035), chemical pleurodesis (10,240), and thoracoscopy (6,615). Hospitalization for lung and breast cancer was more likely to include pleural procedures compared to hospitalization for other types of cancer (59.2 and 65.6%, respectively, p < 0.0001). Chemical pleurodesis through a chest tube compared to thoracoscopic chemical pleurodesis was performed more frequently (57 vs. 43%, p < 0.001) and associated with a longer hospital stay (4.9 vs. 5.9 days, p < 0.001). CONCLUSIONS: Hospital admissions for MPE represent a large burden on the US Health Care System. Many hospitalizations are associated with procedures not expected to reduce the recurrence rate of this condition.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".