Impact of a dedicated malignant pleural effusion clinic on the management of malignant pleural effusion
Bibliographic record
Abstract
Background: Malignant pleural effusions (MPE) are common complications of advanced malignancy. The treatment of MPE is generally focused on palliation of respiratory symptoms. The main drawback of the traditional method of pleurodesis is the requirement for hospitalization. Objectives: To examine the safety, efficacy and economy of management of a dedicated MPE clinic. Methods and analysis: A retrospective cohort study was designed to compare complication rates, pleural effusion control, length of hospital stay, type of interventions, estimated hospital days saved between two three-year non-contemporary periods – before the implementation of MPE clinic and after. Results: Pre MPE clinic and MPE clinic group comprised of 115 and 161 patients respectively. The number of hospital admissions was lower in the MPE clinic period (42.4% vs 73.9%; p<0.0001) and the pleural effusion control was higher in the same group (65.2% versus 51.3%; p=0.02). The estimated hospital days saved was 1001 days over 3 years. Conclusion: A dedicated malignant pleural effusion (MPE) clinic is a strategy of malignant effusion management that reduces hospital admission days and, will allow patients to spend more time outside hospital, reduce costs and save healthcare resources. Outpatient MPE clinic is of particular importance in the setting of the health care system and the overcrowded hospitals. The ability to avoid a one week hospitalization for a palliative intervention and replace it with a simple and effective outpatient procedure should appeal to patients and administrators alike.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".