Understanding the Growing Burden of Malignant Pleural Effusion – Epidemiology, Healthcare Utilization, and Cost: A Canadian Perspective
Bibliographic record
Abstract
INTRODUCTION: Malignant pleural effusion (MPE) is common in advanced malignancy. Data are needed on healthcare utilization and costs. METHODS: This population-based retrospective cohort study included patients aged ≥18 years with an MPE diagnosed between January 1, 2004, and December 31, 2018. We described the incidence and prevalence of MPE from 2004 to 2018 and trends in hospital admissions, emergency department (ED) visits, and pleural-based procedures. In patients diagnosed with MPE from 2006 to 2016, we estimated 2-year healthcare costs (2018 US dollars) from the public payer perspective. RESULTS: The annual incidence and prevalence of MPE increased from 5,090 and 14,579 in 2004 to 6,977 and 23,204 in 2018, respectively. Annual mortality among prevalent patients decreased from 32% to 28%. In 2004, 57% of patients were hospitalized, decreasing to 50% in 2018. ED visits increased (59%-61%). From 2004 to 2018, the number of patients who had insertions of thoracostomy tube (2,211-3,664) and tunneled pleural catheter (383-1,164) increased. The number of patients receiving pleurodesis declined (565-284). No change occurred in the number of patients receiving video-assisted thoracoscopy (range 140-208/year). Between 2004 and 2016, 76,190 patients were diagnosed with MPE (mean age 71.2; 52% female). The median (interquartile range) healthcare cost during follow-up was USD 45,641 (USD 23,237-USD 86,700), with USD 19,753 (USD 9,590-USD 38,746) for inpatient hospitalization. CONCLUSION: Hospitalization and temporizing MPE procedures are associated with high costs. This population-based study may guide clinicians and policymakers to improve MPE management and reduce healthcare costs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".