THE EMPATHS: Nurses at AKUH - Newsletter Quarter 1, 2025
Bibliographic record
Abstract
Message from the Chief Nursing Officer (CNO) Congratulations to President Sulaiman Post-Licensure Compliance Inspection by Sindh Healthcare Commission │ March 18, 2025 10th AKU Annual Surgical Conference │ January 15, and 16 2025 | Nurses Participation 1st International Pediatric Bone Marrow & Stem Cell Transplant Conference on February 15, 2025, at Children’s Hospital Lahore | Oncology Nurses Engagement Chief Nursing Officer Visit to Arab Health & Cleveland Clinic Abu Dhabi, │ January 26 to 31, 2025 Early Childhood Development NBRC Research Advocacy conference │ Nurses Passion Breastfeeding Matters Wins Collaborative Teaching Award Togetherness! Celebrating Eid with Faculty & Staff on Duty Down the Memory Lane Celebrating Class of 2024 Convocation AKUSONAM Class of 2024 Embarks on Their Professional Journey Down the Memory Lane Farewell Learning & Development Central Line Dressing & Management Training Capacity Building Rapid Response Team Cardiology Workshop: leveraging AI to Enhance Nurse Training and to Improve Patient Care | Clifton Medical Services Wound Management Course | Home Health Services Workshop on Staffing Reconciliation; Aligning Personnel and Operations for Enhanced Efficiency Facebook Live session – Nurses Advocacy Role Bridging Technology and Patient Care! Pediatric team Completed Training on AI’s role in Early Detection Session Voluntarily Parental Medical Insurance In Collaboration with HR │February 20, 2025 Alumni Meet & Greet
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.459 | 0.281 |
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".