Impact of a dedicated supportive care center telephone triaging program (SCCTP) for patients with advanced cancer.
Bibliographic record
Abstract
54 Background: Due to high symptom burden in advanced cancer patients, ongoing symptom management for outpatient palliative care patients is vital. More patients are receiving outpatient care; Yet, most palliative care patients receive less than 2 follow ups. Nurse telephone care can improve quality of life in these patients. Our aim was to determine frequency and care provided by Supportive Care Center Telephone Program (SCCTP) in advanced cancer patients. Methods: 400 consecutive patients who utilized palliative care service, 200 from outpatient Supportive Care Center (SCC) and 200 from inpatient Palliative Care (IPC), were followed for 6 months starting 3/2012 to examine call frequency and reason and outcomes including pain and other symptoms [Edmonton Symptom Assessment Scale (ESAS) and Memorial Delirium Assessment Scale (MDAS)] associated with utilization of SCCTP. We also examined the effect of SCCTP interventions on pain, ESAS and counseling needs. Results: 375 patients were evaluable. Median age 59 years, 53% female, 70% white. Most frequent cancer type were gastrointestinal (20%, p < 0.0001) for IPC and thoracic (23%, p <0.0001) for SCC. SCC patients had higher prevalence of CAGE positivity (28% SCC vs 11% IPC, p <0.0001), ESAS SDS(p=0.0134), depression(p=0.0009), anxiety(p=0.0097) and sleep(p=0.0015); MDAS scores were significantly higher in IPC (p<0.0001).115/400 patients (29%) utilized SCCTP. 96/115 outpatients (83%) used the SCCTP vs 19/115 IPC (17%). Common reasons for calls were pain (24%), pain medication refills (24%) and counseling (12%). Of 115 phone calls, 340 recommendations were made; 43% (145/340) were regarding care at home; 56% of these recommendations were regarding opioids. Patients who utilized SCCTP had worse pain(p=0.0059), fatigue(p=0.0448), depression(p=0.0410), FWB(p=0.0149) and better MDAS scores(p=0.0138) compared to non-utilizers. Conclusions: There was more frequent SCCTP use by outpatients than inpatients. Most common reason for utilization was pain control. Frequently, recommendations were made to continue symptom management at home. Patients who utilized SCCTP had worse pain, fatigue, depression, well-being scores and better delirium scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".