Protocol of an iterative qualitative study to develop a molecular testing decision aid for shared decision-making in patients with lung cancer after surgery
Bibliographic record
Abstract
INTRODUCTION: Although molecular testing is crucial for many patients with lung cancer, the decision to carry out molecular testing is not easy to make in actual clinical scenarios. Using a specific decision aid (DA) to conduct shared decision-making (SDM) may help ameliorate this problem. However, no DA currently exists for lung cancer molecular testing (DA_LCMT). We aim to develop an evidence-based, iteratively refined DA, which may facilitate SDM and improve the quality of SDM. METHODS AND ANALYSIS: After considering the Ottawa Decision Support Framework, International Patient Decision Aid Standards and Food and Drug Administration guidance about methods to identify what is important to patients, semistructured interviews with qualitative research methods will be used to generate the decision-making needs of patients with lung cancer diagnosed with lung adenocarcinoma by intraoperative frozen pathological sections. Input will be provided by patients and other stakeholders, including thoracic surgeons, nurses, hospital administrators, molecular testing company staff and insurance company staff. Then, a modified Delphi method will be used to develop the DA_LCMT V.1.0 (DA_LCMT 1.0). Structured interviews with qualitative research methods will be used in the cognitive debriefing (alpha tests) and field testing (beta tests) to revise and improve the DA_LCMT from version 1.0 to the final version, version 3.0. Descriptive statistics will be used to summarise the baseline characteristics of the patients and other stakeholders. Qualitative data will be analysed using the three steps of grounded theory: generate a codebook, update the codebook and create a comprehensive list of related items. ETHICS AND DISSEMINATION: Ethics Committee for Medical Research and New Medical Technology of Sichuan Cancer Hospital approved this study. This protocol is based on the latest version 1.0, dated 31 October 2021. The study was also approved by the Ethics Committees of The Third People's Hospital of Chengdu, Zigong First People's Hospital and Jiangyou People's Hospital. The results of this study will be presented at medical conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05191485.
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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 | 0.099 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".