A treatment trade‐off based decision aid for patients with locally advanced non‐small cell lung cancer
Bibliographic record
Abstract
Purpose To describe the structure and use of a decision aid for patients with locally advanced non-small cell lung cancer (LA-NSCLC) who are eligible for combined-modality treatment (CMT) or for radiotherapy alone (RT). METHODS: The aid included a structured description of the treatment options and trade-off exercises designed to help clarify the patient's values for the relevant outcomes by determining the patient's survival advantage threshold (the increase in survival conferred by CMT over RT that the patient deemed necessary for choosing CMT). Additional outcome measures included each patient's strength of treatment preference, decisional conflict, objective understanding of survival information, decisional role preference, and evaluation of the aid itself. RESULTS: Twenty-five patients met the eligibility criteria for study. Of these, seven declined the decision aid because they had a clear treatment preference (four chose CMT and three chose RT). The remaining 18 participants completed the decision aid; 16 chose CMT and two chose RT. All 18 patients wished to participate in the decision to some extent. All patients reported that using the decision support was useful to them and recommended its use for others. No patient or physician reported that the aid interfered with the physician-patient relationship. Patients' 3-year survival advantage thresholds, and their median survival advantage thresholds, were each strongly correlated with their strengths of treatment preference (rho=0.80, P < 0.001 and rho=0.77, P < 0.001, respectively). For all but one patient, either their 3-year or median survival threshold was consistent with their final treatment choice. Eight patients reported a stronger treatment preference after using the decision aid. CONCLUSIONS: We conclude that a treatment trade-off based decision aid for patients with locally advanced non-small cell lung cancer is feasible, that it demonstrates internal consistency and convergent validity, and that it is favourably evaluated by patients and their physicians. The aid seems to help patients understand the benefits and risks of treatment and to choose the treatment that is most consistent with their values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".