Evaluation of decision support tools for patients with advanced cancer: A systematic review of literature
Bibliographic record
Abstract
OBJECTIVE: Medical decisions in the context of advanced cancer are more based on patient values and preferences than during the early stages of the disease. The implementation of shared decision-making is particularly important with an oncology palliative care population. However, few decision support tools focus on this population. This literature review aims to identify decision support tools related to palliative care for an oncological population and to assess their quality using International Patient Decision Aids Standards criteria. METHOD: The tools were identified through PsycINFO, EMBASE, MEDLINE, and CINAHL databases; the inventory of tools to assist the decisions of the Ottawa Hospital Research Institute; and through the register of Cochrane trials. They were then evaluated using the third version of the International Patient Decision Aids Standards instrument.ResultSixteen tools were identified, which targeted five types of cancer and addressed a particular decision or the use of chemotherapy in addition to palliative care. The quality of the reviewed tools varies.Significance of resultsClinicians can use four decision support tools related to palliative care with an oncology population that meet a certain quality standard. Further studies are needed to develop new decision support tools targeting more types of cancer and decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".