Highlights from ecancer Choosing Wisely Nepal 2022: critical appraisal skills for evidence-based practice, 24th–25th September 2022, Kathmandu, Nepal
Bibliographic record
Abstract
cancer conference organised in Nepal, a Southeast Asian nation sandwiched between India and China. It was focused on critical appraisal skills for evidence-based practice and was organised in partnership with the Karnali Academy of Health Sciences and the Civil Service Hospital from Nepal, and the Queen's Global Oncology Program from Canada. The workshop emphasised the need for critical thinking in understanding clinical research, and also motivated the delegates to undertake meaningful clinical research relevant to the local setting. The sessions highlighted the features of a good clinical research, identify pitfalls in the reporting of clinical trials, implementation of the research into locally relevant practice and development of local clinical guidelines. Furthermore, the faculty also discussed how to write a good scientific paper, the do's and don'ts of a systematic review and meta-analysis, the role of peer-review and how to do one properly and what do editors look for in evaluating papers submitted for publication. The audience learned the importance of finding a good mentor and fostering local and international collaboration. The local faculty also highlighted their own personal journeys and how mentorship and global collaboration played an important role in their own academic career. The enthusiastic panel discussion was a highlight of the programme where the delegates learned about several important topics from the faculties, such as work-life balance, the role of mentorship in building careers and building networks.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".