Supporting Patient Autonomy in Shared Decision Making for Individuals With Head and Neck Cancer
Bibliographic record
Abstract
PURPOSE: Management of head and neck cancer (HNC) can result in substantial long-term, multifaceted disability, leading to significant deficits in one's functioning and quality of life (QoL). Consequently, treatment selection is a challenging component of care for patients with HNC. Clinical care guided by shared decision making (SDM) can help address these decisional challenges and allow for a more individualized approach to treatment. However, due in part to the dominance of biomedically oriented philosophies in clinical care, engaging in SDM that reflects the individual patient's needs may be difficult. CONCLUSIONS: In this clinical focus article, we propose that health care decisions made in the context of biopsychosocial-framed care-one that contrasts to decision making directed solely by the biomedical model-will promote patient autonomy and permit the subjective personal values, beliefs, and preferences of individuals to be considered and incorporated into treatment-related decisions. Consequently, clinical efforts that are directed toward biopsychosocial-framed SDM hold the potential to positively affect QoL and well-being for individuals with HNC.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".