Development a predictive nomogram for spontaneous pleurodesis in patients with non-small cell lung cancer and malignant pleural effusion
Bibliographic record
Abstract
Background: Indwelling pleural catheter (IPC) insertion is associated with fewer subsequent procedures and higher rates of spontaneous pleurodesis (SP) in patients with malignant pleural effusion (MPE). However, long-term pleural drains may cause psychological and physical distress. Additionally, only a portion of patients can benefit from IPC insertion and ultimately have them removed. The nomogram reflects the influence of different factors on outcome visually, enabling clinics to assess the optimal population. Thus, the objective of this study was to develop and validate a novel nomogram to predict successful SP in non-small cell lung cancer (NSCLC) patients with MPE treated with IPC. Methods: We reviewed data on the use of IPC insertion for MPE in patients with NSCLC and allocated them randomly to development (60%) and validation (40%) sets. A static and dynamic nomogram was developed based on multivariate logistic regression to evaluate SP occurrence in the development set. Receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Nelson-Aalen cumulative risk curves were used to validate the predictive accuracy of the nomogram. Results: In total, 331 patients (development set: n=199; validation set: n=132) were selected for this study. Medical thoracoscopy, septated effusion, and effusion volume were the strongest predictors of SP. Other predictors included gender, systemic treatment, and serum neutrophil-to-lymphocyte ratio. The prediction nomogram was demonstrated good predictive ability in the development and validation sets (area under the curve: 0.745 and 0.720, respectively). The DCA indicated that the model had a certain clinical application value. Nelson-Aalen cumulative risk curves showed that the more favorable group received successful SP than did the less favorable group (P<0.001). Conclusions: We developed an accurate and practicable nomogram for successfully predicting SP. These results may benefit clinicians in optimizing treatment decisions, improving the probability of SP, and relieving the long-term discomfort caused by IPC.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".