Inter‐observer variation in image interpretation and the prognostic importance of non‐expansile lung in malignant pleural effusion
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Non-expansile lung (NEL) frequently complicates management of malignant pleural effusion (MPE) and is an important factor in clinical practice and trials. NEL is frequently diagnosed on a single radiographic observation, but neither the inter-observer agreement of this approach nor the prognostic importance of NEL in MPE has been reported. METHODS: A multicentre retrospective cohort study was performed in two UK pleural centres. NEL was defined as <50% pleural re-apposition on post-drainage radiographs by primary and secondary assessors at each site. Inter-observer agreement was assessed by Cohen's kappa (κ). Kaplan-Meier methodology and multivariate Cox models were used to assess the prognostic impact of NEL versus no NEL and 'complete NEL' versus 'complete expansion', based on a single assessor's results from each site. RESULTS: NEL was identified by the primary assessor in 33 of 97 (34%) in Cohort 1 and 15 of 86 (17%) in Cohort 2. Inter-observer agreement between assessors was only fair-to-moderate (Cohort 1 κ: 0.38 (95% CI: 0.21-0.55), Cohort 2 κ: 0.51 (95% CI: 0.30-0.72)). In both cohorts, NEL was associated with shorter median overall survival (Cohort 1: 188 vs 371 days, Cohort 2: 192 vs 412 days). This prognostic association was independent in Cohort 1 (hazard ratio (HR): 2.19, 95% CI: 1.31-3.66) but not in Cohort 2 (HR: 1.42, 95% CI: 0.71-2.87). Survival was inferior in both cohorts in cases of complete NEL versus complete expansion. CONCLUSION: Radiographic NEL is common but inter-observer agreement is only fair-to-moderate. NEL is associated with adverse survival. These data do not support the use of single radiographic assessments to classify NEL.
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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 | 0.036 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".