Chest drain removal pain and its management: a literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 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 it