Optimizing postoperative care protocols in thoracic surgery: best evidence and new technology.
Bibliographic record
Abstract
Postoperative clinical pathways have been shown to improve postoperative care and decrease length of stay in hospital. In thoracic surgery there is a need to develop chest tube management pathways. This paper considers four aspects of chest tube management: (I) appraising the role of chest X-rays in the management of lung resection patients with chest drains; (II) selecting of a fluid output threshold below which chest tubes can be removed safely; (III) deciding whether suction should be applied to chest tubes; (IV) and selecting the safest method for chest tube removal. There is evidence that routine use of chest X-rays does not influence the management of chest tubes. There is a lack of consensus on the highest fluid output threshold below which chest tubes can be safely removed. The optimal use of negative intra-pleural pressure has not yet been established despite multiple randomized controlled trials and meta-analyses. When attempting to improve efficiency in the management of chest tubes, evidence in support of drain removal without a trial of water seal should be considered. Inconsistencies in the interpretation of air leaks and in chest tube management are likely contributors to the conflicting results found in the literature. New digital pleural drainage systems, which provide a more objective air leak assessment and can record air leak trend over time, will likely contribute to the development of new evidence-based guidelines. Technology should be combined with continued efforts to standardize care, create clinical pathways, and analyze their impact on postoperative outcomes.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".