Impact of an outpatient interdisciplinary team (IDT) consultation on symptom clusters in advanced cancer patients seen at a supportive care outpatient clinic (OSC) in a tertiary cancer center
Bibliographic record
Abstract
e20542 Background: Advanced cancer patients develop severe physical and psychosocial symptom clusters. There is limited data on the impact of an outpatient interdisciplinary team (IDT) consultation lead by palliative care specialists on symptom clusters. Cluster composition and consistence, response rate and predictors of response are unknown. Methods: 914 consecutive patients with advanced cancer presenting in the OSC from Jan 2003 to Oct 2008 with a complete Edmonton symptom assessment scale at the initial and follow-up visit (median 14 days, range 1–4 wks), and CAGE status (alcohol screening) were reviewed. Wilcoxon ranked sign test was used to determine whether symptoms changed over time. Principal components factor analysis with varimax rotation was used to determine clusters of symptoms at baseline and at follow-up. The number of factors calculated was determined based upon the number of eigen values that were greater than one. Results: Median age was 59 yrs, female were 46%. The most common primary cancer was Lung (19%). Baseline and follow-up visit scores (mean, SD) were: fatigue 5.7 (2.1) and 5.2 (2.2, p<0.0001), pain 4.9 (2.6) and 4.1 (2.6 p<0.0001), nausea 1.8 (2.4) and 1.7 (2.3, p=0.1), depression 2.6 (2.5) and 2.2(2.4,p<0.0001), anxiety 2.9 (2.7) and 2.4 (2.4, p<0.0001), drowsiness 3.2 (2.8) and 3.2 (2.6, p=0.7), dyspnea 2.6 (2.7) and 2.4 (2.6), p=0.0027), appetite 4.2(2.7) and 3.9 (2.7, p<0.0001), sleep 4.2 (2.6) and 3.8 (2.6, p<0.0001) and well being 4.3 (2.5) and 3.9 (2.3, p<0.0001). During the follow- up the symptom clusters varied from a 3 factor to a 2 factor model, reflecting the impact of the IDT on symptom burden. CAGE positive and CAGE negative patients had a significantly different symptom cluster model. Conclusions: Cluster composition differs when patients are assessed and managed by an IDT and among patients who screen positive for alcoholism. [Table: see text] No significant financial relationships to disclose.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".