Pain in Malignant Pleural Mesothelioma: A Prospective Characterization Study
Bibliographic record
Abstract
INTRODUCTION: Malignant pleural mesothelioma (MPM) is associated with severe pain. The underlying neurobiology of this is complex. The primary aim of this study was to characterize pain in MPM. METHODS: This study was undertaken as part of a trial examining radiotherapy for the treatment of pain in MPM (ISRCTN 10644347). Patients had MPM with associated pain for which radiotherapy was planned and a worst pain score ≥ 4/10. The following assessments were undertaken: clinical neuropathic pain assessment, Brief Pain Inventory (BPI), Leeds Assessment of Neuropathic Symptoms and Signs (LANSS), Short form of the McGill Pain Questionnaire (SF-MPQ), and Quantitative Sensory Testing (QST). The relationship of these characteristics and response to radiotherapy was assessed. Unless stated, medians and interquartile range (IQR) are used. RESULTS: Thirty-seven patients were recruited. Average pain and worst pain was 4 (4-6) and 8 (6-8), respectively. Higher average pain and higher worst pain scores were associated with higher interference scores on the BPI, P < 0.001 and P < 0.0005. Twenty patients (54%) had a clinical diagnosis of neuropathic pain, and of these, only six patients (40%) screened positively for neuropathic pain using the LANSS. Patients with a high LANSS also had higher BPI and SF-MPQs. The presence of neuropathic pain (clinically or by LANSS) did not predict response to radiotherapy, P < 0.05. The SF-MPQ scores were higher in those with abnormal cool sensation on QST (P = 0.016). CONCLUSION: Pain in mesothelioma varies among patients and may have neuropathic components. An adequate pain assessment is necessary to guide the clinician in the appropriate choice of analgesics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".