Characteristics of Patients Reviewed by a Nurse Practitioner within an Outpatient Palliative Radiation Oncology Clinic
Bibliographic record
Abstract
Cancer is a highly prevalent illness in Canada, with half of Canadians developing cancer and one quarter dying of the disease. In spite of significant advancements in cancer diagnosis and treatments, many cancers still have a poor prognosis. Palliative radiotherapy (PRT) is a common and effective therapy for painful bone metastases. Unfortunately, radiation therapy is only offered at specialized cancer centers throughout Canada, and often treatments can be unplanned, time sensitive, and patients have to travel to receive their treatments. Radiation departments are often overwhelmed with patients needing this vital service, leading to decreased quality of care, increased pain and suffering due to a lack of resources and staff to treat this population on a rapid basis. We hypothesized that the addition of a nurse practitioner (NP) to a PRT clinic would improve the functioning of the clinic by increasing efficiency and accessibility. Objectives To prospectively evaluate symptom burden including patient complexity and severity in palliative oncology patients requiring PRT assessed by an NP or radiation oncologist (RO). Methods Patients (PTS) attending the PRT clinic were randomly assessed by the NP or the RO utilizing history, examination, and validated tools (Edmonton Symptom Assessment System [ESAS], Karnofsky Performance Status [KPS], Edmonton Classification System for Cancer Pain [ECS-CP]) to determine eligibility for PRT. Patients assessed by trainees or with missing data were excluded. Data was prospectively entered into an ethics approved database. Results From January 1, 2008 to December 31, 2010, 235 patients had a consultation in the PRT clinic. The NP assessed 137 and 98 were assessed by the RO. When compared between the two providers, patient severity (ESAS, KPS) and complexity (ECS-CP, RT) were not significantly different between those assessed by NP compared to RO. Regarding the patients that received radiation, 72/98 PTS (73%) assessed by RO and 108/137 (79%) assessed by NP, when compared were also not statistically significant. The addition of the NP to this clinic allowed the clinic to complete consultations for 58% (137/235) more patients then if the RO was working alone. Conclusion Our study is the first to quantitatively describe the characteristics, symptom severity, and complexity of patient seen by an NP in a PRT clinic. Our results demonstrate that the addition of the NP to this clinic improved the efficiency and accessibility of these services. Our findings warrant replication in other settings to encourage the greater utilization of NP’s in the Canadian health care system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".