Chest drainage after oesophageal resection: A systematic review
Bibliographic record
Abstract
BACKGROUND: Modern enhanced recovery protocols discourage drain use due to negative impacts on patient comfort, mobility, and recovery, and lack of proven clinical benefit. After oesophagectomy, however, drains are still routinely placed. This review aimed to assess the evidence for, and how best to use chest drains after oesophageal surgery. METHODS: A systematic literature search was performed in Medline, Embase and Cochrane collaboration databases. Studies reporting outcomes for different types or uses of thoracic drainage, or outcomes related to drains after trans-thoracic oesophagectomy 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 with Newcastle-Ottawa and Jadad scores. RESULTS: Among 434 potentially relevant studies, 27 studies met the inclusion criteria and these included 2564 patients. Studies that examined the number of drains showed pain reduction with a single drain compared to multiple drains (3 studies, n = 103), and transhiatal placement compared to intercostal (6 studies, n = 425). Amylase levels may aid diagnosis of anastomotic leak (9 studies, n = 888). Narrow calibre Blake drains may effectively drain both air and fluid (2 studies, n = 163). Drain removal criteria by daily drainage volumes of up to 300 mL did not impact subsequent effusion rates (2 studies, n = 130). Complications related directly to drains were reported by 3 studies (n = 59). CONCLUSION: Available evidence on the impact of thoracic drainage after oesophagectomy is limited, but has the potential to negatively affect outcomes. Further research is required to determine optimum drainage strategies.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".