Introduction of a pain and symptom assessment tool in the clinical setting - lessons learned
Bibliographic record
Abstract
It has long been acknowledged that pain is a subjective, multifaceted phenomenon which is influenced by many factors such as past experience and culture. However there are other symptoms that can be distressing such as dyspnea and nausea. In Ottawa, Canada there was recognition that inconsistencies existed in pain and symptom assessment methods and documentation in the different institutions and agencies when patients with cancer moved from one setting to another as their illness progressed. Therefore, a working group with clinical representatives was formed with a mandate to develop a standardized tool so that there would be a common language for pain and symptom assessment. Although various tools have been developed for pain assessment such as visual analogue or numeric rating scales, there has been limited attention focused on the sustainability of these tools in the practice setting. This paper will focus on the importance of the use of tools for pain and symptom management, issues around implementing them, and sustaining their use in the clinical setting. The Ottawa Pain and Symptom Assessment Record will be used as an exemplar.
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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 | 0.057 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".