Simultaneous care in oncology: Assessment of benefit in relation to symptoms, sex, and age in 753 patients
Bibliographic record
Abstract
Background: Early activation of palliative care for patients with advanced cancer is central in the treatment trajectory. At the Veneto Institute of Oncology, a simultaneous-care outpatient clinic (SCOC) has been active since 2014, where patients are evaluated by an oncologist together with a palliative care team. Recently, we reported on consecutive patients admitted at SCOC from 2018 to 2021 in terms of appropriateness, process, and outcome indicators. Here, we report further analysis in the same group of 753 patients, evaluating other parameters and the correlation between symptom intensity, gender, age, and survival. Methods: SCOC data were retrieved from a prospectively maintained database. Results: Among the patients, 42.2% were women, and the median age was 68 years, with 46.7% of patients aged ≥70 years. The most prevalent disease type was gastrointestinal cancer (75.2%), and 90.9% of the patients had metastatic disease. The median score for the distress thermometer was 4; the vast majority of the patients (98.6%) reported physical problems, and 69.4% presented emotional issues. Younger women demonstrated a significantly greater median distress than other patients (p=0.0018). Almost all symptoms had a higher prevalence on the 0-3 Edmonton Symptom Assessment Scale (ESAS) score, except for fatigue. About 43.8% of the patients received systemic anticancer treatment (SAT) in the last 60 days of life, 15.0% of whom received SAT in the last month and 3.1% in the last 2 weeks. For some symptoms, women frequently had more ESAS >3. Pain and nausea were significantly less reported by older patients compared with younger adults. Men had a lower risk of having MUST score ≥ 2 (p=0.0311). Men and older patients showed a lower prognosis awareness (p=0.0011 and p=0.0049, respectively). Older patients received less SAT within the last 30 days of life (p=0.0006) and had death risk decreased by 20.0%. Conclusion: Our study identified two subgroups of patients with advanced cancer who require special attention and support due to important symptoms' burden detected by Patient Reported Outcome Measures tests: women and younger adults. These categories of patients require special attention and should be provided early access at SCOC. The role of an oncologist remains crucial to intercept all patients in need of early palliative care and balancing trade-offs of anticancer treatment in advanced metastatic disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".