Clinically important associations of pleurodesis success in malignant pleural effusion: Analysis of the TIME1 data set
Bibliographic record
Abstract
ABSTRACT Background and objective Chemical pleurodesis is performed for patients with MPE with a published success rate of around 80%. It has been postulated that inflammation is key in achieving successful pleural symphysis, as evidenced by higher amounts of pain or detected inflammatory response. Patients with mesothelioma are postulated to have a lower rate of successful pleurodesis due to lack of normal pleural tissue enabling an inflammatory response. Methods The TIME1 trial data set, in which pleurodesis success and pain were co‐primary outcome measures, was used to address a number of these assumptions. Pain score, systemic inflammatory parameters as a marker of pleural inflammation and cancer type were analysed in relation to pleurodesis success. Results In total, 285 patients were included with an overall success rate of 81.4%. There was a significantly higher rise in CRP in the Pleurodesis Success group compared with the Pleurodesis Failure group (mean difference: 19.2, 95% CI of the difference: 6.2–32.0, P = 0.004) but no significant change in WCC. There was no significant difference in pain scores or analgesia requirements between the groups. Patients with mesothelioma had a lower rate of pleurodesis success than non‐mesothelioma patients (73.3% vs 84.9%, χ2 = 5.1, P = 0.023). Conclusion Change in CRP during pleurodesis is associated with successful pleurodesis but higher levels of pain are not associated. Patients with mesothelioma appear less likely to undergo successful pleurodesis than patients with other malignancies, but there is still a significant rise in systemic inflammatory markers. The mechanisms of these findings are unclear but warrant further investigation.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".