Systematic screening of perception of curability among patients with advanced cancer: A longitudinal analysis.
Bibliographic record
Abstract
12110 Background: Clinicians do not routinely assess patients’ illness understanding despite its importance in decision making. Systematic screening of illness understanding is a novel approach that normalizes discussion of this sensitive topic, helps identify patients with information needs and allows clinicians to monitor and support their patients’ understanding over time. In this study, we examined the changes in perception of curability over time in patients who completed systematic screening at our supportive care clinic (SCC). Methods: We implemented universal electronic systematic screening of illness understanding in our SCC using two questions from the Prognosis and Treatment Perception Questionnaire at consultation and every 2 months. We included all advanced cancer patients who completed screening at their consultation and at least one follow-up visit within one year. The primary outcome was patients’ perception of curability, which was categorized as accurate if they reported the likelihood of cure as < 25%. Patients were grouped into one of four categories based on responses at their first and last SCC visits: accurate-accurate, accurate-inaccurate, inaccurate-accurate and inaccurate-inaccurate. We examined patient characteristics associated with the inaccurate-inaccurate group versus all others using univariate and multivariate logistic regression analysis. Results: 432 patients (mean age 58 [SD 13], female n=248 [57.4%], white n=331 [76.6%]) were included. The mean number of SCC visits was 2.69 [SD 0.9] and the median duration between the first and last SCC visits was 157 days [IQR 129-194]. At visits 1, 2, 3, 4 and 5+, 34% [147/432], 37% [159/432], 36% [71/197], 38% [30/78] and 46% [11/24] of patients had an accurate understanding of their curability (p=0.24), respectively. Comparing the first and last visit, 233 [54%] were inaccurate-inaccurate, 119 [28%] were accurate-accurate, 52 [12%] were inaccurate-accurate and 28 [6%] were accurate-inaccurate. Asian race and greater well-being at baseline were associated with being inaccurate-inaccurate (Table). Conclusions: Systematic screening identified that only ~1 in 3 advanced cancer patients had an accurate understanding of their curability at SCC consultation and this did not improve significantly over time. Certain subgroups were more likely to remain inaccurate at last follow-up. Our findings highlight the need to systematically screen for illness understanding and to work towards bridging information gaps with better communication and coping support. Multivariate analysis of patient characteristics associated with being in the inaccurate-inaccurate group. Patient Characteristic Odds Ratio 95% CI p-value Race (versus White) Asian 3.92 1.52-12.2 0.009 Black or African American 1.91 0.90-4.26 0.1 Edmonton Symptom Assessment System Well-Being 0.82 0.73-0.93 0.003
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".