Development and Validation of a Personalized, Web-Based Decision Aid for Lung Cancer Screening Using Mixed Methods: A Study Protocol
Bibliographic record
Abstract
BACKGROUND: The National Lung Screening Trial demonstrated that low-dose computed tomography (LDCT) screening could be an effective way to reduce lung cancer mortality. Informed decision-making in the context of lung cancer screening requires that potential screening subjects accurately recognize their own lung cancer risk, as well as the harms and benefits associated with screening, while taking into account their personal values and preferences. OBJECTIVE: Our objective is to develop a Web-based decision aid in accordance with the qualifying and certification criteria in the International Patient Decision Aid Standards instrument version 4.0 that will assist patients in making informed decisions with regard to lung cancer screening. METHODS: In "alpha" testing, a prototype of the decision aid was tested for usability with 10 potential screening participants in focus groups. Feedback was also sought from public health and health risk communication experts external to the study. Following that, improvements to the prototype were made accordingly, and "beta" testing was done in the form of a quasi-experimental design-a before-after study-with a group of 60 participants. Outcomes tested were knowledge, risk perception of lung cancer and lung cancer screening, decisional conflict, and acceptability of the decision aid as determined by means of a self-administered electronic survey. Focus groups of a subsample of survey participants will be conducted to gain further insight into usability issues. RESULTS: Alpha testing is completed. Beta testing is currently being carried out. As of 2014 December 7, 60 participants had completed the before-after study. We expect to have results by 2015 January 31. Qualitative data collection and analysis are expected to be completed by 2015 May 31. CONCLUSIONS: We hypothesize that this Web-based, interactive decision aid containing personalized, graphical, and contextual information on the benefits and harms of LDCT screening will increase knowledge, reduce decisional conflict, and improve concordance between patient preferences and the current US Preventive Services Task Force's screening guidelines.
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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 | 0.077 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".