A clinical decision support tool to improve care planning in end-of-life cancer care
Bibliographic record
Abstract
In end-of-life cancer care, nurse clinicians strive to deliver an effective care plan to minimise pain, manage debilitating symptoms, and improve patients' quality of life as they approach end of life. Sadly, existing research literature is replete with studies contesting that sub-optimal pain and symptom management remains the reality for many end-of-life cancer patients, resulting in unnecessary suffering and a diminished quality of life. To begin to overcome the chasm between inadequate pain and symptom management currently delivered in end-of-life care, and the excellent standard of care that could be achieved, nurse clinicians need only leverage knowledge from existing knowledge resources. Researchers assert that clinical decision making for care planning in end-of-life cancer care can be improved by ensuring access to contextually relevant, medical knowledge resources at the point-of-care. Collectively, these medical knowledge resources represent a valuable asset which can improve clinical decisions and the delivery of best clinical practice, while reducing medical uncertainty, unnecessary clinical variation, and medical errors. In this research we present a clinical decision support system to bridge the knowledge gap that currently impedes the planning, and delivery, of effective pain and symptom management in end-of-life cancer care. This system enables nurse clinicians to: (a) make informed clinical decisions based upon the patient's presenting situation, care priorities, preferences, and standards of care; and (b) create personalised care plans for effective pain and symptom management in end-of-life cancer 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 | 0.017 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".