Challenges in lung cancer multidisciplinary collaboration experienced by specialists in four countries.
Bibliographic record
Abstract
e23002 Background: The importance and challenges of Multidisciplinary Team (MDT) collaboration in managing lung cancer have been increasingly recognized in an ever more complex therapeutic environment. Data on physicians’ viewpoints regarding MDT collaboration in lung cancer care, collected in a broader study assessing challenges related to lung/thyroid cancer patient management, are presented. Methods: A mixed-methods approach was used to analyze this data, combining qualitative interviews and a quantitative survey. Pulmonologists (PLM), oncologists (ONC) and pathologists (PTH) from Germany (GE), Japan (JP), the United Kingdom (UK) and the United States (US) were recruited. Results: A total of 44 specialists participated in interviews and 377 in a survey. Quantitative data reveal that 53% of pulmonologists in JP and 39% in GE have suboptimal knowledge of the timing of patient referral to an oncologist. Fewer PLM/PTH from JP (43%/47%) report a fully integrated MDT team approach in their clinical setting, compared to those from GE (80%/95%), the UK (96%/82%) and the US (82%/97%). Qualitative data indicate that current MDT team practices are perceived as delaying patient care due to significant inefficiencies (sometimes due to lack of knowledge/skills) and unclear responsibilities within the team. Around half of ONC in each country and 78% of PLM from the UK report a gap in knowledge and relevance of each genetic biomarker test according to clinical presentation. PTH in the UK (70%), the US (50%), and JP (72%) report sub-optimal skills identifying biomarker tests to inform the progression of lung cancer (also a challenge for PLM/ONC). Conclusions: This study indicates a need for multi-level interventions addressing systemic and attitudinal barriers as well as knowledge gaps which affect physicians’ ability to collaborate in lung cancer care.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".