038 Development of Multidisciplinary Symptom Management Tools Using The ADAPTE Approach
Bibliographic record
Abstract
Background Evidence-based tools for symptom management were developed to assist healthcare providers in comprehensive assessment and appropriate management of symptoms. These evidence-based tools consist of comprehensive guides-to-practice, quick reference pocket guides, algorithms and smart phone apps. Objectives The symptom management tools promote an interdisciplinary model of care that enables early identification and assessment of symptoms, appropriate documentation and communication regarding symptoms, optimal symptom management, and coordinated care throughout the illness trajectory. The tools are intended to be user-friendly, and are available in print, web and smart phone applications. Methods The tools were developed by an interdisciplinary panel of healthcare providers using the ADAPTE guideline adaptation approach. This included a literature search for recent guidelines and systematic reviews, guideline appraisal using the AGREE tool and selection, and in some instances modification, of recommendations. Expert feedback was obtained and subsequently appropriate revisions were made. Results The symptom management tools provide recommendations based on the best available evidence and expert consensus, for assessing, determining aetiology, diagnosing potential problems and for recommending non-pharmacological and pharmacological interventions. Implications for Guideline Developers/Users The ADAPTE approach offers a comprehensive and rapid process of developing evidence-based tools for the cancer patient population. Following the global trend of creating user-friendly clinical guidance, the guides-to-practice, quick reference pocket guides, algorithms and smart phone apps are an innovative set of tools that are accessible to a diverse group of care providers, in a manner that would suit the individual’s clinical needs.
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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 | 0.072 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".