Evaluation of a multi-disciplinary, systematic approach to palliative care for patients undergoing active treatment for head and neck cancer.
Bibliographic record
Abstract
91 Background: Patients who receive concurrent chemoradiation therapy for head and neck cancer (H&N CA) commonly experience a range of symptoms, including dysphagia, weight loss, and dehydration, which may impact their treatment and prognosis. Treatment of patients with H&N CA often involves multiple specialties, including Surgery, Medical, and Radiation Oncology, and Palliative Care. In January 2017, Kaiser Permanente Colorado (KPCO) began providing early, integrated palliative care for all H&N CA patients (stages I-IV) starting chemoradiation at a single clinic site. This quality improvement program was subsequently evaluated for ease of implementation and, secondarily, to discern impacts on patient care. Methods: Retrospective chart review was performed for patients undergoing treatment in 2017 for H&N CA with concurrent chemoradiation therapy who also received integrated palliative care (n = 16) compared to standard care patients (n = 32) from 2015-2017. Data on rates of unplanned hospitalizations and feeding tube placement, completion of Medical Durable Power of Attorney (MDPOA) and Edmonton Symptom Assessment Scale (ESAS) were extracted from the electronic health record. Descriptive and inferential statistics were used to review data. Assessment of implementation occurred by interviews with the clinical teams. Results: Implementation of the program required intentional work between departments to define roles and reduce unwanted overlap. Standardized nursing assessments of symptom burden were developed. Patients followed in an integrated fashion had increased MDPOA and ESAS completion (38% vs 6%) and decreased hospital admissions (19% vs 53%) and unplanned PEG tube insertions (13% vs 34%). Differences were statistically significant for hospitalizations (p = 0.022) and ESAS completion (p = 0.006). Conclusions: Development of integrated patient management required intentional inter-department team communication and did not disrupt the clinic environment of any department. Lower hospitalization rates may be related to increased monitoring of symptoms. Based on this experience, the program was expanded to other clinical sites within KPCO.
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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 | 0.047 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".