Needs Assessment for a Decision Aid in Oral Cancer Requiring Major Resection and Reconstructions
Bibliographic record
Abstract
OBJECTIVE: Treatment of advanced oral cavity cancer necessitates ablative and reconstructive surgery that can be life-altering, creating nuanced priorities between the desire for survival and quality of life. This study sought to describe currents practice of shared decision-making among patients with advanced oral cavity cancer and determine the need for a decision support tool. STUDY DESIGN: Cross-sectional, convergent, mixed methods study from 2020 to 2023. SETTING: Two major Canadian academic centers. METHODS: Semi-structured interviews were conducted and interpreted via inductive thematic analysis of preoperative and postoperative patients with locoregionally advanced oral cavity cancer. Qualitative findings were integrated with data obtained from validated instruments that examined shared decision-making (SDM-Q-9), decisional conflict (Decisional Conflict Scale; DCS), and decision-making self-efficacy (Ottawa Decision Self-Efficacy; ODSE). RESULTS: The median age of the 37 participants was 67 years (SD: 11). Qualitative themes suggested that the following influenced care decisions: (1) approaches to information delivery, (2) preoperative experiences impact decision-making, (3) perceived knowledge gaps and negative emotions, and (4) fear of cancer. Seven patients (18.9%) had clinically significant decisional conflict (DCS > 25) despite high levels of decisional self-efficacy (mean ODSE 95.1, SD:7.9). Greater perception of shared decision-making (r = -0.328, P = .048) and decisional self-efficacy (r = -0.687, P < .001) were correlated with lower decisional conflict. Recommendations for future decision-making tools included: (1) wide accessibility, (2) timeline of treatment events, (3) incorporate components that activate shared decision-making and integrate the clinician-patient dyad, and (4) promote conversation around difficult topics. CONCLUSION: These findings support the need for integrated and improved tools to promote shared decision-making.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".