The impact of a simplified documentation method for the Edmonton Classification System for Cancer Pain (ECS-CP) on clinician utilization.
Bibliographic record
Abstract
e21625 Background: The use of standardized pain classification systems such as the ECS-CP can assist in the assessment and management of cancer pain. However, its completion has been limited due to the perceived complexity of decoding the individual symbols of each feature. The objectives of this study were to determine the rate of clinician documentation and completion of the ECS-CP features after revision and simplification of the response for each feature. Methods: Electronic records of consecutive patient visits at the outpatient supportive care center seen by 12 palliative medicine specialists were collected during three study periods: 6 months before(pre-interventional period), 6 months and 24 months after (post-interventional period) the implementation of the simplified ECS-CP tool. Rate of ECS-CP documentation, completion, and analysis of patient and physician predictors were completed. Results: 1012 patients’ documentation was analyzed: 343 patients before, 341 six months after, and 328 twenty four months after the intervention. ≥ 2/5 items were completed before the intervention, 6 months after the intervention and 24 months after intervention in 0/343 (0%), 136/341 (40%), and 238/328 (73%) respectively [p < 0.001]. 5/5 items were completed before the intervention, 6 months after the intervention and 24 months after intervention in 0/343 (0%), 131/341 (38%), and 222/328 (68%) respectively [p < 0.001]. There were no patient or physician predictors found to be significant for successful documentation of ECS-CP features. Conclusions: Our findings suggest that significant simplification of the scoring system and intensive education is necessary for successful adoption of a scoring system. More research is needed in order to identify how to adopt tools for daily clinical practice in palliative care.
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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 | 0.021 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".