Transferring With TACT
Bibliographic record
Abstract
BACKGROUND: Neonatal patients who no longer require level IV neonatal intensive care unit care are transferred to less acute levels of care. Standardized assessment tools have been shown to be beneficial in the transfer of patient care. However, no standardized tools were available to assist neonatal providers in the assessment and communication of the infants needs at transfer. PURPOSE: The purpose was to develop a Transfer Assessment and Communication Tool (TACT) that guides provider decision making in the transfer of infants from a level IV neonatal intensive care unit to a less acute level of care within a regionalized healthcare system. METHODS: Phase 1 included developing the first draft of the TACT using retrospective data, known variables from published literature, and study team expertise. In phase 2, the final draft of the TACT was created through feedback from expert neonatal providers in the regionalized care system using e-Delphi methodology. RESULTS: The first draft of the TACT, developed in phase 1, included 36 characteristics. In phase 2, nurses, nurse practitioners, and physician experts representing all levels of newborn care participated in 4 e-Delphi surveys to develop the final draft of the TACT, which included 74 weighted characteristics. IMPLICATIONS FOR PRACTICE AND RESEARCH: Potential benefits of the TACT include improved communication across healthcare teams, reduced risk for readmission, and increased caregiver visitation. The next steps are to validate the TACT for use either retrospectively or in real time, including characteristic weights, before implementation of this tool in the clinical setting.
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 | 0.019 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".