Shared Decision-Making in the Surgical Management of Rectal Cancer: Comparing Low Anterior Resection and Abdominoperineal Resection Using a Patient Decision Aid
Bibliographic record
Abstract
Low rectal cancer is surgically managed with one of two primary procedures: low anterior resection (LAR) or abdominoperineal resection (APR). Each procedure has a unique profile of potential complications, oncologic outcomes, and quality-of-life impacts. The acceptability of these outcomes is highly driven by patient values. Consequently, shared decision-making is essential to selecting the optimal procedure for each patient. Evidence has shown that patient decision aids (PtDAs) improve patient knowledge, reduce decisional conflict, and support value-congruent decisions. This review describes the development of a rectal cancer PtDA for the choice between LAR and APR. This PtDA was designed according to the International Patient Decision Aid Standards and Ottawa Decision Support Framework. Evaluation of this rectal cancer PtDA demonstrated increased patient knowledge, reduced decisional conflict, and enhanced patient preparedness for decision-making. Despite strong evidence for their utility, PtDAs remain underutilized. This review highlights key barriers in implementing PtDAs and proposes strategies to facilitate the effective integration of PtDAs into surgical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".