Bibliographic record
Abstract
Objective: Multiple forces in today’s hospital environment have increased the frequency of transitions of care for patients. This increase has resulted in the need to establish effective and structured handover processes. Methods: Our institution established a multi-disciplinary Handover Taskforce with the goal of creating a practical and HIPAA-compliant hand-off tool utilizing computer technology. The Taskforce had representation from residents, attending physicians, nursing, pharmacy, informatics, risk management and patient safety. The Taskforce work was guided by two concepts. First, streamline the process by avoiding redundant input into clinical information systems. Second, create an effective tool that could be used by all members of the healthcare team. Results: We describe the process by which this project was successfully executed, resulting in a HIPAA-compliant custom web application utilizing Microsoft SharePoint ®. Conclusions: Challenges encountered in the process, as well as applicability to other institutions, are addressed in the article.
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 | 0.040 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".