Long-term Follow-up
Research gap analysis derived from 2 education papers in our local library.
The gap
The impact of interventions on long-term outcomes and recurrence rates in various medical conditions is not adequately addressed, requiring systematic longitudinal studies.
Consensus across the literature
Papers collectively leave open the need for long-term follow-up to assess the durability and effectiveness of their respective interventions.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 2 representative gaps
- Application of self-organised learning environments integrated with generative AI in standardised training for residents (2026) · doi
Several limitations should be acknowledged when interpreting the present findings. First the absence of pre-intervention digital literacy scores and detailed process data on actual AI tool utilization (including usage frequency, duration, task type, and completion rate of monthly themes) precluded adjustment for the potential confounding effects of baseline characteristics and AI tool usage on digital literacy outcomes. Second, lack of process indicators and follow-up was restricted to the training period, and long-term effects on clinical competence and digital literacy were not assessed; thus, an extended follow-up period is needed to examine the sus- tained impact of the model. Third, the model was developed using a specific AI platform, and its performance may vary when applied with alternative AI systems, suggesting that future studies could compare the effectiveness of different tools within the same frame- work. Finally, subgroup analyses were not performed in the present study, and further research is required to identify the population that may benefit most from this intervention. Nevertheless, these limitations do not detract from the main conclusions of this study. clinical skills, digital literacy, and learning satisfaction, and may rep- resent a feasible approach to inform the digital and intelligent trans- formation of medical education.
Keywords: digital literacy limitations present intervention process tool usage effects follow period clinical model several acknowledged - A multimodal deep learning-based dynamic prediction model for colorectal cancer liver metastasis (2026) · doi
Future work should extend follow-up periods and assess the impact of model application on clinical decision-making and outcomes in prospective intervention studies.
Keywords: future extend follow periods assess impact model application clinical decision making outcomes prospective intervention
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