A clustered randomized controlled trial of symptom screening and automatic referral for supportive care for patients with GI cancer care needs.
Bibliographic record
Abstract
Purpose: To explore the impact of implementation of a symptom screening and supportive/palliative care referral pathway in patients newly referred to a Canadian gastrointestinal medical oncology clinic. Methods: Eighty-eight subjects were recruited in each study arm. Intervention subjects were assessed by a member of the supportive/palliative care team if they had a severity score of >3/10 on the Edmonton Symptom Assessment System. Controls received normal care, including discretionary referral. Symptom severity was assessed over the subsequent five months. Data on survival, care setting of death (home, hospice or hospital) and long-term resource use were also collected. Results: Screening led to 141 specialist supportive/palliative care visits in the intervention arm versus only nine in the control arm. There were, however, no subsequent significant differences in symptom severity or the long-term outcomes measured. Many patients identified by the >3/10 severity threshold did not need/want specialist supportive/palliative care referral, and those with severe distress were either identified by the oncology team already or were too unwell or overwhelmed to participate in the study. The specialist service was not overwhelmed. Important considerations on timing and mode of administration of screening tools were revealed. Conclusion: Routine symptom screening can be burdensome for oncology patients and needs to be as simple as possible. Triaging positive screens is an important role for oncology nurses. Investment in training oncology teams to manage uncomplicated distress in the oncology clinic allows for optimal use of scarce supportive/palliative care specialist resources for patients with complex needs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".