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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".