The influence of a cachexia clinic on palliative care integration in oncology.
Bibliographic record
Abstract
124 Background: Appetite and weight loss are common in patients with advanced cancer and specialized cachexia clinics have been established to address these symptoms. Given the association between anorexia/cachexia and other adverse symptoms, these patients may also benefit from specialty level palliative care (PC). However, referral to outpatient specialty level PC is often delayed or does not occur. We sought to examine the prevalence of other factors associated with appetite and weight loss in patients with advanced cancer and the impact of a specialized cachexia clinic on identification and treatment of other PC needs. Methods: The records of patients referred by their Oncologist to the cachexia clinic of a cancer center from August 2016 to June 2017 were reviewed retrospectively. Subjects who had been referred to PC by their Oncologist were excluded. Patients had been assessed for symptom burden using the Edmonton Symptom Assessment Scale (ESAS-r). Patients identified with PC needs had been referred to the PC clinic for follow-up within 30 days after cachexia clinic consultation. Results: Thirty subjects were evaluated in the cachexia clinic (average age 68 years; 63% female). The predominant diagnosis was lung cancer (70%). An average of 6 symptoms per patient were in the moderate to severe range on ESAS, excluding appetite. Depression, fatigue and pain were most common. The average cachexia clinic total ESAS score was 51.61. Only 17% of patients had completed advance directives. Ninety-three % of patients were referred to PC and 68% were seen. The average number of PC visits was 2.79. Within the PC clinic, advance directive completion increased to 37%, goals of care discussion occurred with 50% and 17% received hospice referrals. At the most recent follow-up in the PC clinic, the average total ESAS score had decreased by 11.44 (22%) and all ESAS item scores were improved on average. Conclusions: The cachexia clinic proved a useful means to identify other PC needs and achieve effective PC referrals. We suggest this is proof of concept that specialty clinics can be a meaningful way to achieve an earlier entry point to comprehensive PC in patients who were not previously referred by their Oncologists.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".