Bibliographic record
Abstract
Clara de Necker, BCur, obtained her BCur degree in Nursing Science, specialized in Critical Care, where she was awarded best student of the year for general critical care. She furthered her career into Case Management, awarded the National Case Manager of the Year Award twice in a row, after which she started her own Clinical Consultancy business. This included case management as a function for Sleepnet, coordinating the Home Ventilation Program. She is currently the Health Information Support Manager in the Health Information Management Department for Mediclinic International, skilled in multiple clinical coding systems across all business platforms, while continuing her part-time involvement with case management for the Home Ventilation Program. Address correspondence to Clara de Necker, BCur, Suite 146, P/B X3018, Strand, SA 7140 ([email protected]). Note: Many of us have the pleasure of meeting case managers both across the country and around the world at the CMSA Annual Conferences year after year. Our colleagues travel from South Africa, Australia, England, Germany, South Korea, Malaysia, and Canada. This issue's column was written by Clara de Necker, a case manager hailing from South Africa. The story should be relatable to many readers and proves that the heart of case management beats across borders.The intent of this column is meant to speak to the heart of case management: our joys, our struggles, and our lessons learned. Please send your thoughts and ideas to us so we may include them in future articles. Mindy Owen at: [email protected]. Teri Treiger at: [email protected].The author reports no conflicts of interest.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.595 | 0.468 |
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".