Chest drain removal pain and its management: a literature review
Bibliographic record
Abstract
AIMS AND OBJECTIVES: The purpose of this review was to analyse critically the published research on chest drain removal pain and its management. The findings of descriptive and non-pharmacological intervention studies were summarized and studies of analgesic efficacy were critiqued in depth. BACKGROUND: The removal of a chest drain is a painful and frightening experience, particularly for children. However, there is limited research regarding the amount of pain experienced or effectiveness of analgesia for this procedure. RESULTS: Fourteen studies were reviewed, including five descriptive studies; three studies of non-pharmacological interventions; and six randomized controlled trials of morphine, local anaesthetics and Entonox. The search revealed only two paediatric studies. Many of the studies had design limitations or were poorly reported. The majority of studies indicated that patients experienced moderate to severe pain during chest drain removal, even when morphine or local anaesthetics were given. CONCLUSIONS: Morphine alone does not provide satisfactory analgesia for chest drain removal pain. Non-steroidal anti-inflammatory drugs, local anaesthetics and inhalation agents may have a role to play in providing more effective analgesia for this procedure. RELEVANCE TO CLINICAL PRACTICE: Analgesic protocols for the management of painful procedures such as chest drain removal are unsatisfactory and practice in this area should be revised. More research is needed to determine the efficacy of drugs other than morphine, particularly Entonox and to investigate multi-modal techniques of management further.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".