Assessing symptom burden in patients with advanced COPD – Is CAT enough?
Bibliographic record
Abstract
Background: CAT (COPD Assessment Test) is a validated tool to assess the impact of COPD symptoms. Edmonton Symptom Assessment Scale (ESAS) was developed to assess common symptoms in palliative care patients (pain, tiredness, nausea, depression, anxiety, drowsiness, appetite disturbance, poor well being and shortness of breath). ESAS may help identify and quantify significant non-respiratory symptoms in this population and guide palliative care. Aims: 1) To assess prevalence and severity of symptoms in patients with severe COPD using both CAT and ESAS 2) To assess if CAT/ESAS correlate with mortality in order to target advance care planning (ACP) in this population. Method: Patients with severe COPD (MRC grade 4-5 breathlessness ± FEV <30% predicted) completed ESAS and CAT scores on attendance at a tertiary palliative COPD clinic (n=43). Linear regression analysis was performed to compare scores and Chi square analysis to compare survival. Results: Mean age 69.7. Mean FEV1 = 43% predicted (SD ± 13.7). Mean CAT score was high 28 (SD ±7.3). 4 patients died within 12 months of attendance. There was no significant trend between CAT and ESAS scores. Patients reported, along with breathlessness, high levels of tiredness, low wellbeing and anxiety. There was large variation in drowsiness, pain and nausea scores. CAT >30 and ESAS >50 were associated with mortality at 12 months in this small population. Conclusions: ESAS delineated significant symptom burden not accounted for in CAT assessment of patients with advanced COPD. A broader assessment tool such as ESAS in combination with conventional measures (CAT, FEV1 and MRC grade) is advocated in a holistic approach to palliating end stage COPD and may help target ACP.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".