Calculation of individual expected pleural drainage from total body lymph flow: a guide for fast-tracking removal of chest drains
Bibliographic record
Abstract
We read with great interest the recent original research article by Bjerregaard et al. [1]. By reporting pertinent 30-day outcomes for fasttrack chest tube removal following video-assisted thoracoscopic (VATS) lobectomy with <500 ml in 24 h, this paper has the potential to change thoracic surgical practice regarding safe acceptable upper limits of pleural fluid drainage. As mentioned in the Discussion section on study limitations, we propose that it may be interesting for the authors to pursue their 24-h pleural fluid output data prior to chest tube removal. It is unclear how many of the analysed patients actually had pleural fluid drainage approaching the stated upper limit of 500 ml in 24 h. If they are not a majority, this might change the ultimate study conclusion, with implications for patient management. Understanding that the national database employed for this research limited the authors to providing data on ‘<500 ml in 24 h’, we would like to know if the authors consider it feasible to conduct an institutional level chart review on their reported cases with the following two goals:
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".