Overuse of small chest drains for pleural effusions: a retrospective practice review
Bibliographic record
Abstract
Purpose Small-bore drains (≤ 16 Fr) are used in many centers to manage all pleural effusions. The goal of this study was to determine the proportion of avoidable chest drains and associated complications when a strategy of routine chest drain insertion is in place. Design/methodology/approach We retrospectively reviewed consecutive pleural procedures performed in the Radiology Department of the McGill University Health Centre over one year (August 2015–July 2016). Drain insertion was the default drainage strategy. An interdisciplinary workgroup established criteria for drain insertion, namely: pneumothorax, pleural infection (confirmed/highly suspected), massive effusion (more than 2/3 of hemithorax with severe dyspnea /hypoxemia), effusions in ventilated patients and hemothorax. Drains inserted without any of these criteria were deemed potentially avoidable. Findings A total of 288 procedures performed in 205 patients were reviewed: 249 (86.5%) drain insertions and 39 (13.5%) thoracenteses. Out of 249 chest drains, 113 (45.4%) were placed in the absence of drain insertion criteria and were deemed potentially avoidable. Of those, 33.6% were inserted for malignant effusions (without subsequent pleurodesis) and 34.5% for transudative effusions (median drainage duration of 2 and 4 days, respectively). Major complications were seen in 21.5% of all procedures. Pneumothorax requiring intervention (2.1%), bleeding (0.7%) and organ puncture or drain misplacement (2%) only occurred with drain insertion. Narcotics were prescribed more frequently following drain insertion vs. thoracentesis (27.1% vs. 9.1%, p = 0.03). Originality/value Routine use of chest drains for pleural effusions leads to avoidable drain insertions in a large proportion of cases and causes unnecessary harms.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".