Routine Edmonton Symptom Assessment System (ESAS) scores in epithelioid malignant pleural mesothelioma (eMPM) patients undergoing palliative systemic therapy.
Bibliographic record
Abstract
96 Background: Palliative chemotherapy, the mainstay for incurable eMPM, is offered to patients with good standing performance status (ECOG 0-1). We determined the proportion of eMPM patients experiencing moderate (Mod) to severe (Sev) symptoms while on palliative chemotherapy using ESAS symptom scores at diagnosis and during first and subsequent lines of systemic therapy. Methods: ESAS scores (0=no symptom; 10=worst symptom) were routinely captured in eMPM patients at Princess Margaret (Toronto, Canada). Retrospective chart review collected ESAS, clinical, treatment and outcome data. ESAS scores and proportions were summarized within baseline, firstline, and subsequent lines of therapy. Mod symptom was defined as ESAS scores of 4-6, and Sev as 7-10. ESAS scores between treatment lines were analyzed by Mann-Whitney U tests. Locally weighted smoothing (LOESS) plots assessed ESAS scores over treatment duration. Results: Of 37 palliative eMPM patients, the most frequent baseline symptoms were fatigue (33% Mod, 25% Sev), dyspnea (27% Mod; 17% Sev), anxiety (21% Mod; 21% Sev) and depression (34% Mod; 4% Sev). Overall well-being was poor in 45% of patients (31% Mod; 14% Sev). Compared to baseline, patients on firstline therapy reported increased pain ( p = 0.002), poor appetite ( p = 0.005) and nausea ( p = 0.02); decreased depression ( p = 0.03) and anxiety ( p = 0.006); and stable well-being ( p = 0.27). In patients well enough for subsequent therapy, patients reported reduced drowsiness ( p = 0.01) and improved overall well-being ( p = 0.04) compared to firstline therapy. LOESS plot patterns were similar between firstline and subsequent lines of treatment. Conclusions: eMPM patients had high symptom scores at the time of diagnosis. However, patients’ depression and anxiety decreased when starting initial treatment, and general well-being was stable. Of patients fit enough for subsequent lines of therapy, there were improvements in drowsiness and overall well-being. When selectively used, palliative systemic therapy of all lines are associated with some improved symptoms, but pain, poor appetite, and nausea need to be monitored and managed more effectively.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".