Follow-up Visits and Changes in Pain Scores Reported by Oncology Outpatients After Initial Presentation With Severe Pain
Bibliographic record
Abstract
BACKGROUND: In addition to tumour treatment, the management of symptoms such as pain is an important component of cancer care. Pain management is a complex field and prior studies have highlighted many different clinical care responses to a cancer patient presenting with severe pain. We explored follow-up and how pain screening scores changed over time, among a cohort of cancer outpatients, and how follow-up was scheduled after the initial visit. METHODS: The care provided to 96 patients seen at the London Regional Cancer Program was reviewed for the 12-week period following presentation with severe pain >7/10. Follow-up ESAS (Edmonton Symptom Assessment System) scores, visits, and compliance were documented. RESULTS: Follow-up ESAS data was available for 41/96 patients. Mean ESAS pain decreased from 8.4/10 to 3.6/10 among those patients with follow-up; however, for 55/96 patients, no follow-up ESAS score was available (deceased n=3, no follow-up visit n=41, no pain score reported, n=11). CONCLUSIONS: Despite a very high proportion of documented active pain management plans in the case of cancer patients presenting with severe pain, very little follow-up directed specifically at pain management was performed. Cancer treatment appears to be the primary determinant of oncology follow-up timing at our centre.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".