Postthoracotomy Ipsilateral Shoulder Pain: A Literature Review on Characteristics and Treatment
Bibliographic record
Abstract
Context.Postthoracotomy Ipsilateral Shoulder Pain (IPS) is a common and sometimes intractable pain syndrome. IPS is different from chest wall pain in type, origin, and treatments. Various treatments are suggested or applied for it but none of them is regarded as popular accepted effective one.Objectives.To review data and collect all present experiences about postthoracotomy IPS and its management and suggest future research directions.Methods.Search in PubMed database and additional search for specific topics and review them to retrieve relevant articles as data source in a narrative review article.Results.Even in the presence of effective epidural analgesia, ISP is a common cause of severe postthoracotomy pain. The phrenic nerve has an important role in the physiopathology of postthoracotomy ISP. Different treatments have been applied or suggested. Controlling the afferent nociceptive signals conveyed by the phrenic nerve at various levels—from peripheral branches on the diaphragm to its entrance in the cervical spine—could be of therapeutic value. Despite potential concerns about safety, intrapleural or phrenic nerve blocks are tolerated well, at least in a selected group of patient.Conclusion.Further researches could be directed on selective sensory block and motor function preservation of the phrenic nerve. However, the safety and efficacy of temporary loss of phrenic nerve function and intrapleural local anesthetics should be assessed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".