Impact of a Pleural Care Program on the Management of Patients With Malignant Pleural Effusions
Bibliographic record
Abstract
BACKGROUND: Malignant pleural effusions (MPEs) are common and associated with a poor prognosis. Yet, many patients face suboptimal management characterized by repeated, nondefinitive therapeutic procedures and potentially avoidable hospital admissions. METHODS: We conducted a retrospective comparison of patients who underwent a definitive palliative intervention for MPE (indwelling pleural catheter or pleurodesis) at our center, before and after the implementation of a pleural care program. Targeted interventions included staff education, establishment of formal pleural drainage policies, a pleural clinic with weekday walk-in capacity, and a rapid access pathway for oncology patients. Outcomes assessed were the proportion of emergency room (ER) presentations, hospitalizations, number of nondefinitive pleural procedures, and time-to-definitive palliative procedure. RESULTS: A total of 144 patients were included: 69 in the preintervention group and 75 in the postintervention group. Although there was no difference in the proportion of ER presentations before and after interventions (43.5% vs. 38.7%, P =0.56), hospital admissions declined significantly (47.8% vs. 24.0%, P =0.003). The proportion of patients undergoing chest drain insertion decreased significantly (46.4% vs. 13.3%, P <0.001), with a stable low number of nondefinitive procedures per patient (1.6±1.1 vs. 1.3±0.9, P =0.32). A 7-day decrease in median time from presentation-to-definitive palliative procedure ( P =0.05) was observed. CONCLUSION: A targeted pleural care program improved MPE palliation through reduction in hospitalizations and chest drain use, and shorter time-to-definitive palliation, despite failing to reduce ER presentations.
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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 | 0.008 |
| 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 | 0.001 |
| 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".