Patient Decision Aid for Chemotherapy or Exclusion in Cisplatin-Intolerant Patients With Locally Advanced Cervical Cancer: Development, Alpha Testing, and Peer Validation
Bibliographic record
Abstract
PURPOSE: In locally advanced cervical cancer (LACC), adding cisplatin to radiotherapy (RT) improves survival but increases toxicity. Among patients with cisplatin contraindications, RT compliance may be compromised by toxicity because of cisplatin or a substitute. In shared decision making, a patient decision aid (PtDA) may decrease decisional conflict and attitudinal barriers, thereby improving treatment compliance. METHODS: Following International Patient Decision Aid Standards (IPDAS) guidelines, a steering committee of two radiation oncologists, a gynecologic oncologist, an oncology nurse, a clinical psychologist, a cancer survivor, and a caregiver developed the chemotherapy or exclusion in cisplatin-intolerant patients with LACC (CECIL) prototype, a PtDA for cisplatin-intolerant patients with LACC deciding about adding chemotherapy to RT. The prototype was alpha-tested using the e-Delphi method. The patient decision aid research group Ottawa Acceptability Questionnaire was used to evaluate comprehensibility, length, amount of information, neutrality, and overall suitability for decision making. The prototype was then independently evaluated by local internal, local external, and international reviewers using the IPDAS checklist version 4, which encompasses information, probabilities, values, guidance, development, evidence, disclosure, plain language, and evaluation. RESULTS: Alpha testing showed high practitioner acceptability (all items with mean and median scores ≥4; overall mean score 4.70 of 5.00) and good patient acceptability (all items rated good to excellent). Content validation showed that the PtDA satisfied all IPDAS six qualifying and six certification criteria, with high overall mean score (3.63 of 4.00) for all 17 applicable quality criteria. CONCLUSION: The CECIL prototype shows good practitioner and patient acceptability, and content validity on peer review. Clinical testing to determine its effectiveness in reducing decisional conflict is ongoing.
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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 | 0.123 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".