Utilization and Impact of a Radiation Nursing Clinic to Address Acute Care Needs for Patients with Gynecologic Cancers
Bibliographic record
Abstract
Background: The risk factors for acute care utilization in gynecologic oncology patients are poorly understood. This study aimed to evaluate risk factors for the utilization of our centre’s acute care radiation nursing clinic (RNC) by gynecologic oncology patients receiving radiotherapy (RT). Methods: This was a retrospective cohort study of gynecological cancer patients treated with RT at an academic cancer centre between 1 August 2021 and 31 January 2022. Data on socio-demographics, clinical and treatment characteristics, and RNC visits were collected and summarized by descriptive statistics. The Wilcoxon rank sum test and chi-squared test/Fisher’s exact test were used for comparisons of continuous and categorical variables, respectively. Results: RT was delivered to 180 patients, of whom 42 (23%) received concurrent chemoradiation (CCR). Compared to those receiving RT alone, patients receiving CCR had higher rates of RNC utilization (55% vs. 19%, p < 0.001). Within the CCR cohort, patients who presented to the RNC were more likely to be unpartnered (43% vs. 11%, p = 0.04), receive a referral to Psychosocial Oncology (39% vs. 5.3%, p = 0.01), and experience treatment interruptions (52% vs. 16%, p = 0.02). There were no associations between RNC visits and age, disease site, or distance from the cancer centre. Conclusions: The receipt of CCR and specific psychosocial risk factors were associated with increased RNC utilization. Targeted strategies and early intervention to better meet the supportive care and psychosocial needs of this vulnerable population are needed.
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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 | 0.004 |
| 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 | 0.000 |
| 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".