Patients' preferred and perceived level of involvement in decision making for cancer treatment: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Patient involvement in decision making is conditional for personalised treatment decisions. We aim to provide an up-to-date overview of patients' preferred and perceived level of involvement in decision making for cancer treatment. METHODS: A systematic search was performed in PubMed, EMBASE, PsycINFO and CINAHL for articles published between January 2009 and January 2020. Search terms were 'decision making', 'patient participation', 'oncology', 'perception' and 'treatment'. Inclusion criteria were: written in English, peer-reviewed, reporting patients' preferred and perceived level of involvement, including adult cancer patients and concerning decision making for cancer treatment. The percentages of patients preferring and perceiving an active, shared or passive decision role and the (dis)concordance are presented. Quality assessment was performed with a modified version of the New-Castle Ottawa Scale. RESULTS: 31 studies were included. The median percentage of patients preferring an active, shared or passive role in decision making was respectively 25%, 46%, and 27%. The median percentage of patients perceiving an active, shared or passive role was respectively 27%, 39%, and 34%. The median concordance in preferred and perceived role of all studies was 70%. Disconcordance was highest for a shared role; 42%. CONCLUSIONS: Patients' preferences for involvement in cancer treatment decision vary widely. A significant number of patients perceived a decisional role other than preferred. Improvements in patient involvement have been observed in the last decade. However, there is still room for improvement and physicians should explore patients' preferences for involvement in decision making in order to truly deliver personalised 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 | 0.020 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".