Forecasting pulmonary air leak duration following lung surgery using transpleural airflow data from a digital pleural drainage device
Bibliographic record
Abstract
BACKGROUND: Prolonged air leak (PAL) is often the limiting factor for hospital discharge after lung surgery. Our goal was to develop a statistical model that reliably predicts pulmonary air leak resolution by applying statistical time series modeling and forecasting techniques to digital drainage data. METHODS: Autoregressive Integrated Moving Average (ARIMA) modeling was used to forecast air leak flow from transplural air flow data. The results from ARIMA were retrospectively internally validated with a group of 100 patients who underwent lung resection between December 2012 and March 2017, for whom digital pleural drainage data was available for analysis and a persistent air leak was the limiting factor for chest tube removal. RESULTS: The ARIMA model correctly identified 82% (82/100) of patients as to whether or not the last chest tube removal was appropriate. The performance characteristics of the model in properly identifying patients whose air leak would resolve and who would therefore be candidates for safe chest tube removal were: sensitivity 80% (95% CI, 69-88%), specificity 88% (95% CI, 68-97%), positive predictive value 95% (95% CI, 86-99%), and negative predictive value 59% (95% CI, 42-79%). The false positive and false negative rate was 12% (95% CI, 12-31%) and 20% (95% CI, 12-31%). CONCLUSIONS: We were able to validate a statistical model that that reliably predicted resolution of pulmonary air leak resolution over a 24-hour period. This information may improve the care of patients with chest tube by optimizing duration of pleural drainage.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".