Surveillance of Lung Cancer and Mesothelioma Patients With Noncurative Treatment Intent
Bibliographic record
Abstract
BACKGROUND: Lung cancer patients with advanced disease and no active treatment options currently face frequent follow-up visits to outpatient clinics, associated with significant anxiety, time commitment, and costs. Visits also place considerable strain on the health system. Evidence from other cancers and chronic health conditions suggests virtual or remote follow-up can lead to higher patient satisfaction without negatively impacting health outcomes such as survival time. OBJECTIVE: The aim of this review was to identify patient preferences for, and any evidence of relative effectiveness of, different surveillance protocols for patients who have noncurative treatment intent for lung cancer or mesothelioma. INTERVENTIONS/METHODS: MEDLINE, PubMed, and CINAHL Plus databases were searched for articles published between 1998 and June 2018. The search was restricted to English-language publications and included all original research. RESULTS: Nine studies met the inclusion criteria, with most studies being retrospective. Findings identified the need for reassurance and hope as part of surveillance, the importance of trust and relationship, and the lack of consistency and evidence around frequency and method of surveillance models. CONCLUSIONS: Current surveillance is based on expert opinion with little consideration of patient preferences, quality of life, impact on anxiety, and impact on survival outcomes. IMPLICATIONS FOR PRACTICE: Nurses play a key role in managing surveillance programs for noncurative lung cancer patients. Programs should be built using codesign approaches to ensure best outcomes. Further research needs to be conducted, ensuring directed surveillance models that meet the holistic needs of patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".