A New Method for Performing Continuous Manometry during Pleural Effusion Drainage
Bibliographic record
Abstract
BACKGROUND: Pleural manometry can predict the presence of trapped lung and guide large-volume thoracentesis. The current technique for pleural manometry transduces pressure from the needle or intercostal catheter, necessitating intermittent cessation of fluid drainage at the time of pressure recordings. OBJECTIVES: To develop and validate a technique for performing continuous pleural manometry, where pressure is transduced from an epidural catheter that is passed through the drainage tube to sit within the pleural space. METHODS: Pleural manometry was performed on 10 patients undergoing thoracentesis of at least 500 ml, using the traditional intermittent and new continuous technique simultaneously, and pleural pressures were recorded after each drainage of 100 ml. The pleural elastance (PEL) curves and their 95% confidence intervals (CIs), derived using measurements from each technique, were compared using the analysis of covariance and Student's paired t test, respectively. RESULTS: There was no significant difference in PEL calculated using each method (p > 0.1); however, there was a trend towards the CI for the PEL derived from the continuous method being narrower (p = 0.08). Fully automated measurement of drainage volume and pleural pressure, with real-time calculation and display of PEL, was achieved by connecting the system to a urodynamics machine. CONCLUSIONS: Pleural manometry can be transduced from an epidural catheter passed through the drainage tube into the pleural space, which gives continuous recording of the pleural pressure throughout the procedure. This allows for automated calculation and display of the pleural pressure and PEL in real time, if the system is connected to a computer with appropriate software.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".