Preferred and actual involvement of caregivers in oncologic treatment decision-making: A systematic review
Bibliographic record
Abstract
Introduction Treatment decision-making in cancer is complex and many patients bring their caregiver to appointments to help them make those decisions. Multiple studies show the importance of involving caregivers in the treatment decision-making process. We aimed to explore the preferred and actual involvement of caregivers in the decision-making process of patients with cancer and to see if there are age or cultural background related differences in caregiver involvement. Materials and Methods A systematic review of Pubmed and Embase was performed on January 2, 2022. Studies containing numerical data regarding caregiver involvement were included, as were studies describing the agreement between patients and caregivers regarding treatment decisions. Studies assessing solely patients aged younger than 18 years old or terminally ill patients, and studies without extractable data were excluded. Risk of bias was assessed by two independent reviewers using an adapted version of the Newcastle-Ottawa scale. Results were analysed in two separate age groups, one <62 years and one ³62 years. Results Twenty-two studies with a total of 11,986 patients and 6,260 caregivers were included in this review. A median of 75% of patients preferred caregivers to be involved in decision-making and a median of 85% of caregivers preferred to be involved. With regards to age groups, the preferred involvement of caregivers was more frequent in the younger study populations. With regards to geographical differences, studies performed in Western countries showed a lower preference for caregiver’s involvement compared to studies from Asian countries. A median of 72% of the patients reported the caregiver was actually involved in the treatment decision-making and a median of 78% of the caregivers reported they were actually involved. The most important role of caregivers was to listen and provide emotional support. Discussion Patients and caregivers both want caregivers to be involved in the treatment decision-making process and most caregivers are actually involved. An ongoing dialogue between clinicians, patients and caregivers about decision-making is important to meet the individual patient’s and caregiver’s needs when involved in the decision-making process. Important limitations were a lack of studies in older patients and significant differences in outcome measures among studies.
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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 | 0.030 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".