Point of Care Use of a Personal Digital Assistant for Patient Consultation Management
Bibliographic record
Abstract
The development and integration of a personal digital assistant (PDA)-based point-of-care database into an intravenous resource nurse (IVRN) consultation service for the purposes of consultation management and service characterization are described. The IVRN team provides a consultation service 7 days a week in this 1000-bed tertiary adult care teaching hospital. No simple, reliable method for documenting IVRN patient care activity and facilitating IVRN-initiated patient follow-up evaluation was available. Implementation of a PDA database with exportability of data to statistical analysis software was undertaken in July 2001. A Palm IIIXE PDA was purchased and a three-table, 13-field database was developed using HanDBase software. During the 7-month period of data collection, the IVRN team recorded 4868 consultations for 40 patient care areas. Full analysis of service characteristics was conducted using SPSS 10.0 software. Team members adopted the new technology with few problems, and the authors now can efficiently track and analyze the services provided by their IVRN team.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".