Validation of content of clinical cases in obstetric medicine for a shared web-based educational tool
Bibliographic record
Abstract
Subsequent to the validation of a Canadian Curriculum blueprint for Obstetric Medicine (OM), the Canadian Consensus for a Curriculum in Obstetric Medicine (CanCOM) research group was approached to develop 20 cases to address gaps in clinical exposure during clinical rotations in OM. Forty-nine Obstetric Internists were identified and 43 confirmed their affiliation to the group. Participants (N = 22) reviewed the content of the CanCOM blueprint and identified curriculum content that they considered essential for a rotation for senior General Internal Medicine residents. This survey led to the creation of the CanCOM II essential content blueprint for General Internal Medicine. Following this step, a second subgroup of participants (N = 21) participated in a Delphi survey to identify the content that should be addressed by a teaching case for senior General Internal Medicine residents. A high-level of consensus was obtained for 13 topics and a moderate level for the 7 subsequent topics resulting in the creation of the CanCOM II clinical cases available at http://gemoq.ca/cancom-ii-clinical-case-databank/.
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 | 0.189 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".