Advance care planning in adults with oral cancer: Multi‐institutional <scp>cross‐sectional</scp> study
Bibliographic record
Abstract
PURPOSE: Patients undergoing head and neck surgery are often elderly and frail with significant comorbidities. Discussion and documentation of what patients would desire for end-of-life care and decision-making is, therefore, essential for delivering patient-centered care. MATERIALS AND METHODS: This was a retrospective, cross-sectional study of patients undergoing surgery for head and neck cancer at two large, academic, tertiary care centers in Canada. Advance care planning was defined as any documentation of advance directives, resuscitation orders, or end-of-life care preferences. RESULTS: Among 301 patients, advance care planning was documented for 31 (10.3%). Patients with locally advanced disease (T3+) were twice as likely to have advance care planning documentation compared to those with early disease (RR 1.97, 95%CI [0.98, 3.97]). CONCLUSIONS: In this multi-institutional cross-sectional study of two large academic centers, we have demonstrated that advance care planning and documentation is overall poor in patients undergoing surgery for oral cancer. These findings may have health policy implications, as advance care planning is associated with increased patient and provider satisfaction and improved alignment of patient goals and care delivered. Future work will investigate barriers and facilitators to advance care-planning documentation in this setting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".