Developing a decision support tool for responding to patients’ reported levels of information needs, family anxiety, depression, and breathlessness.
Bibliographic record
Abstract
173 Background: Routine clinical use of Patient Reported Outcome Measures (PROMs) such as the Palliative Care Outcome Scale (POS) may be prevented by a lack of guidance on how to respond to reported symptoms. When using POS in clinical care, clinicians encounter the most difficulties with responding to information needs, depression and family anxiety while breathlessness remains a difficult to treat symptom. We aimed to create a Decision Support Tool (DST) on how to respond to different levels of these patient-reported symptoms. Methods: A systematic search for guidelines and systematic reviews on these topics was conducted (in Pubmed, Cochrane and York DARE databases, Googlescholar, NICE, National Guideline Clearinghouse, Canadian Medical Association, Google.com). In a two-round online Delphi study purposefully sampled international experts (clinicians, researchers, patient representatives) judged the appropriateness (1-9 scale + do not know option) of drafted recommendations for each POS answer category (0-4) and provided qualitative remarks. Recommendations with a median of 7-9 and <30% of scores between 1-3 and 7-9 were included in the DST. Quality was assessed using an adapted GRADE approach. Results: Twenty-five out of 38 (66%) experts participated in round 1, 23 out of 37 (62%) in round 2. Higher POS scores were related to more included recommendations. The DST consists of both a manual and flow-charts of included recommendations for each topic. Overall, psychosocial interventions were recommended for lower levels of depression and breathlessness than drug interventions (e.g., goal-setting/coping versus morphine for breathlessness). Good communication and emotional support were recommended for low family anxiety levels, but a social needs assessment only for higher levels. For information needs recommendations were least discriminative; almost all recommendations (e.g., assess patients’ understanding of information, show empathy) seemed always relevant. Conclusions: The developed DST can assist clinical responses to patient-reported symptoms in palliative care. Future work is needed to test the effect of using the DST on patients’ outcomes.
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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 | 0.087 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".