Development of a Patient Decision Aid for Rectal Cancer Patients with Clinical Complete Response after Neo-Adjuvant Treatment
Bibliographic record
Abstract
Surgery is the primary component of curative treatment for patients with rectal cancer. However, patients with a clinical complete response (cCR) after neo-adjuvant treatment may avoid the morbidity and mortality of radical surgery. An organ-sparing strategy could be an oncological equivalent alternative. Therefore, shared decision making between the patient and the healthcare professional (HCP) should take place. This can be facilitated by a patient decision aid (PtDA). In this study, we developed a PtDA based on a literature review and the key elements of the Ottawa Decision Support Framework. Additionally, a qualitative study was performed to review and evaluate the PtDA by both HCPs and former rectal cancer patients by a Delphi procedure and semi-structured interviews, respectively. A strong consensus was reached after the first round (I-CVI 0.85-1). Eleven patients were interviewed and most of them indicated that using a PtDA in clinical practice would be of added value in the decision making. Patients indicated that their decisional needs are centered on the impact of side effects on their quality of life and the outcome of the different options. The PtDA was modified taking into account the remarks of patients and HCPs and a second Delphi round was held. The second round again showed a strong consensus (I-CVI 0.87-1).
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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 | 0.039 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".