Highlights from the Choosing Wisely 2022 for Resource Limited Settings: Reducing Low Value Cancer Care for Sustainability conference, 17th–18th September, Mumbai, India
Bibliographic record
Abstract
The 'Choosing Wisely 2022' conference, organised by the ecancer foundation, was held at the Tata Memorial Hospital, Mumbai, India, on 17 and 18 September. It was a successful event with 159 delegates attending it in person and around 328 delegates attending online. Thirty oncology experts from across the world shared their thoughts during this meeting. The theme of the conference was to focus on cancer care, in low- and middle-income countries (LMICs). The emphasis of discussion was on ways to select more cost-effective and high value treatments and interventions and minimise financial toxicity. In addition, cancer research from LMICs needs to be improved substantially. Collaboration and networking amongst cancer institutions in LMICs is essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".