Similarities and differences of lung cancer (LC) multidisciplinary teams (MDTs) in China compared to Europe and Canada.
Bibliographic record
Abstract
e13562 Background: LC MDTs involve a collaborative approach where specialists work together to analyze individual cases, discuss treatment options, and tailor comprehensive care plans for patients. Implementing MDTs can be challenging, especially for developing countries with limited resources and high disease burden. China, with approximately 820,000 new patients with lung cancer in 2020 accounting for 37% of the total number in the world, bears the responsibility of treating the largest lung cancer patient population worldwide. The global MDT Aid Program (MAP) in LC has been including China to gather insights on the challenges, best practices, similarities, and differences of China LC MDT processes compared to other countries, so as to improve the outcome and cost-efficiency of global MDTs. Methods: In this study, we learned about the status, best practices, challenges of LC MDT development in China through virtual semi-structured healthcare professional (HCP) interviews, on-site masterclasses, and internal data sharing with 3 Chinese leading hospitals in LC - Guangdong Provincial People’s Hospital, West China Hospital and Xiangya Hospital. Similarities and differences were gathered based on previous MAP experience and insights from HCP discussions in LC across the Netherlands, Italy, Spain, Switzerland, Denmark, Norway, and Canada. To build a global collaboration on LC MDTs, three international knowledge exchange (IKE) sessions were organised to include China and all participating countries. Results: We identified seven main differences between China, Europe and Canada, including the enrolment criteria of patients in MDT that China MDTs focus on challenging cases such as resistance to multi-lines of therapy, multiple primary lung cancer and stage III lung cancer, while Europe and Canada MDTs mainly for routine patients. Other differences include setup and composition of MDT teams, digitalization of MDTs, incentives and reimbursement for MDT attendance, cross-institutional patient referral to the MDT, time spent per patient, patient follow-up after MDT discussion. Similarities identified include the MDT development model from both hospital and national level, experience sharing to educate junior physicians, hospital willingness to learn at global level, and challenges of high pressure with limited medical resources. Best practices of leveraging innovative digital capabilities to improve MDT efficiency and to support decision-making have been shared by China participants during the IKEs. Conclusions: Global collaboration to identify synergies within LC MDT communities can help China and other regions learn from each other and share China expertise in large-volume data-based practices globally. Alongside China’s rapid development of digital health, the digitalization of MDTs is likely to follow.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".