Determining optimal air leak resolution criteria when using digital pleural drainage device after lung resection
Bibliographic record
Abstract
Objective: There is limited clinical evidence to support any specific parenchymal air leak resolution criteria when using digital pleural drainage devices following lung resection. The aim of this study is to determine an optimal air leak resolution criteria, where duration of chest tube drainage is minimized while avoiding complications from premature chest tube removal. Methods: Airflow data averaged at 10-minute intervals was collected prospectively using a digital pleural drainage device (Thopaz; Medela) in 400 patients from 2015 to 2019. All permutations of air leak resolution criteria from <10 to 100 mL/minute for 4 to 12 hours were applied retrospectively to the pleural drainage data to determine air leak duration, and air leak recurrence frequency and volume. Air leak recurrence indicates potential for rather than occurrence of adverse events. Descriptive statistics were used to identify the optimal criteria based on patient safety (low frequency and volume of air leak recurrences), and efficiency (shortest initial air leak duration). Results: < .0001). Conclusions: When using a digital pleural drainage device, a postoperative air leak resolution criteria <50 mL/minute for 8 consecutive hours was associated with the best safety and efficiency profile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".