Intrapleural fibrinolytic therapy (IPFT) in loculated pleural effusions—analysis of predictors for failure of therapy and bleeding: a cohort study
Bibliographic record
Abstract
OBJECTIVES: To assess risk factors associated with failure and bleeding in intrapleural fibrinolytic therapy (IPFT) for pleural effusions. DESIGN: Retrospective case series. SETTING: Two tertiary-care centres in North America. PARTICIPANTS: We identified 237 cases that received IPFT for the treatment of pleural effusions. Data for 227 patients were compiled including demographics, investigations, radiological findings pretherapy and post-therapy and outcomes. INTERVENTION: Fibrinolytic therapy in the form of tissue plasminogen activator (t-PA) or streptokinase. PRIMARY AND SECONDARY OUTCOMES: Success of therapy is defined as the presence of both clinical and radiological improvement leading to resolution. Failure was defined as persistence (ie, ineffective treatment) or complications requiring intervention from IPFT. Incidence of bleeding post-IPFT, identifying factors related to failure of therapy and bleeding. RESULTS: IPFT was used in 237 patients with pleural effusions; 163 with empyema/complicated parapneumonic effusions, 32 malignant effusions and 23 with haemothorax. Overall, resolution was achieved in 80% of our cases. Failure occurred in 46 (20%) cases. Multivariate analysis revealed that failure was associated with the presence of pleural thickening (>2 mm) on CT scan (p=0.0031, OR 3, 95% CI 1.46 to 6.57). Bleeding was not associated with any specific variable in our study (antiplatelet medications, p=0.08). CONCLUSIONS: Pleural thickening on a CT scan was found to be associated with failure of IPFT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".