Initial Pain Management Plans in Response to Severe Pain Indicators on Oncology Clinic Previsit Questionnaires
Bibliographic record
Abstract
BACKGROUND: The issue of how to address patient pain in the outpatient setting remains challenging. At the London Regional Cancer Program (London, Ontario), patients complete the Edmonton Symptom Assessment System (ESAS) before most visits. OBJECTIVES: To perform a chart review assessing the frequency and, if applicable, the type of a clinical care plan that was developed if a patient indicated pain ≥7 on a 10-point scale. METHODS: The charts of 100 eligible sequential outpatient visits were reviewed and the initial pain management approaches were documented. RESULTS: Between December 2011 and May 2012, visits by 7265 unique patients included 100 eligible visits (pain ≥7 of 10). In 83 cases, active pain management plans, ranging from counselling to hospital admission, were proposed. Active pain management plans were more likely if the cause was believed to be cancer⁄treatment related: 63 of 65 (96.9%) versus 20 of 35 (57.1%, noncancer⁄unknown pain cause); P<0.001. There were no differences depending on cancer treatment intent or medical service. CONCLUSIONS: Active pain management plans were documented in 83% of visits. However, patients who reported severe pain that was assessed as benign or unknown in etiology received intervention less frequently, perhaps indicating that oncologists either consider themselves less responsible for noncancer pain, or believe that pain chronicity may lead to a higher ESAS pain score without indicating a need for acute intervention. Further study is needed to determine the subsequent effect of the care plans on patient-reported ESAS pain scores at future clinic visits.
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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 | 0.013 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".