714 UTILITY, TECHNIQUES, AND OUTCOMES FOR THORACIC DRAINAGE AFTER OESOPHAGECTOMY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract The routine use of post-operative drains in surgery continues to evolve as part of modern practices. Modern enhanced recovery protocols eschew using abdominal drains due to their impact on patient comfort, mobility, and recovery. This change in practice has not applied to thoracic drainage after oesophagectomy, where one or multiple drains are routinely placed. The aim of this study was to determine the evidence for, and how best to use drains during oesophageal surgery. Methods A systematic literature search was performed in Medline, Embase and Cochrane collaboration databases until Jan 25th, 2021. All studies which compared outcomes for different types or uses of thoracic drainage, or reported outcomes directly related to chest drains in oesophageal surgery were included. Studies were collated into domains based on variations in number, position, type, removal criteria, diagnostic use and complications of drains. Methodological quality was assessed by the Newcastle-Ottawa and Jadad Scores. Results 28 studies met the inclusion criteria. Four studies compared drain numbers, three showed similar outcomes and pain reduction using one. A single study showed that another, ‘anastomotic drain’ aided diagnosis and reduced leak mortality. Transhiatal drains had less pain and similar outcomes compared to intercostal drains. Drain fluid amylase aids leak diagnosis, however, accuracy requires drains to remain for 6 days. Removal of drains with daily volumes of less than 300 mL did not impact effusion rate. Complications can arise from drains with a 7% chance of drains migrating into the lumen of a leak and a risk of drain-site metastasis. Conclusion Drain use is a small facet of oesophageal surgery that can have a significant impact on outcomes. There is no evidence for non-drain use. A single transhiatal drain reduces pain without impacting on outcomes. Drains can have a role in diagnosing and managing anastomotic leaks, however, to be accurate drains have to stay in situ for longer. This extends patients discomfort and moves away from ERP trends and other surgical specialities.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".