A Pilot Of Telemedicine Supportive Care Integrated Into A Rural Oncology Clinic
Bibliographic record
Abstract
IntroductionDespite the rapid growth in palliative care (PC) services, regions remain without access to specialty palliative care. Community-based telemedicine may offer solutions to underserved populations from rural areas within the United States.1,2MethodsRetrospective review of 22 patients managed in a rural oncology clinic from a University hospital via telemedicine. Consecutive patients over 9 months, with active, advanced cancer referred for symptom management, transitions of care, or both. Care coordinated by the University PC service and oncology clinic, included nurses, nurse practitioner, and oncologist. Regulatory, legal, information technology (IT), and systems logistics were developed in partnership for 6 months prior to pilot. Edmonton Symptom Assessment Scale (ESAS) recorded at each visit.ResultsAverage age 66, predominately female with metastatic solid tumors. Patients had 1-3 telemedicine visits. Most common symptom was pain, median score 6. Morphine equivalent daily dose averaged 65. Most common opioids were oxycodone, transdermal fentanyl, and extended release morphine. Three visits required physical exam support from onsite providers (one for dermatological, 2 for neurologic exams). Six visits required immediate controlled substance prescriptions (other prescriptions were mailed; non controlled prescriptions sent electronically). Goals of care discussion in 45% (n=10) and advance care planning documents reviewed when applicable. Technological issues occurred in 2 visits and resolved without IT involvement.ConclusionsOur pilot program integrated specialist palliative care into a rural oncology clinic providing supportive care via telemedicine, including symptom management and goals of care discussions. Further research should define optimal integration of PC telemedicine into rural oncology clinics.Sources: 1) Menon PR, Stapleton RD, et al. Telemedicine as a tool to provide family conferences and palliative care consultations in critically ill patients at rural health care institutions: a pilot study. Am J HospPalliatCare. 2015 Jun;32(4):448-53.2)Watanabe SM, Fairchild A, PituskinE, et al. Improving access to specialist multidisciplinary palliative care consultation for rural cancer patients by videoconferencing: report of a pilot project. Support Care Cancer. 2013 Apr;21(4):1201-7.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".