Use of tunneled pleural catheters for outpatient treatment of malignant pleural effusions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Malignant pleural effusion is a common complication in advanced malignancy that causes debilitating symptoms which result in impaired quality of life. The primary therapeutic goal in malignant pleural effusion management is effective palliation of the associated respiratory symptoms. Pleurodesis by chest tube or thoracoscopy is widely accepted as the gold standard treatment, although these treatments are not without problems. Tunneled pleural catheters represent a new safe and effective outpatient treatment option for these patients, with no reported mortality and minimal morbidity. RECENT FINDINGS: Chest tube insertion with talc slurry and thoracoscopy with talc insufflation are effective methods for achieving spontaneous pleurodesis, although associated with significant morbidity and mortality. A growing body of evidence is confirming that long-term palliation of malignant pleural effusion can be achieved by using tunneled pleural catheters in a large proportion of relatively unselected patients on an outpatient basis. SUMMARY: The optimal method for palliative management of malignant pleural effusion remains controversial. The high success rates, low complication rates and efficacy in patients with a wide range of performance status support the use of tunneled pleural catheters as a first-line treatment for symptomatic malignant pleural effusion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".