Critical care procedure logging using handheld computers.
Bibliographic record
Abstract
INTRODUCTION: We conducted this study to evaluate the feasibility of implementing an internet-linked handheld computer procedure logging system in a critical care training program. METHODS: Subspecialty trainees in the Interdepartmental Division of Critical Care at the University of Toronto received and were trained in the use of Palm handheld computers loaded with a customized program for logging critical care procedures. The procedures were entered into the handheld device using checkboxes and drop-down lists, and data were uploaded to a central database via the internet. To evaluate the feasibility of this system, we tracked the utilization of this data collection system. Benefits and disadvantages were assessed through surveys. RESULTS: All 11 trainees successfully uploaded data to the central database, but only six (55%) continued to upload data on a regular basis. The most common reason cited for not using the system pertained to initial technical problems with data uploading. From 1 July 2002 to 30 June 2003, a total of 914 procedures were logged. Significant variability was noted in the number of procedures logged by individual trainees (range 13-242). The database generated by regular users provided potentially useful information to the training program director regarding the scope and location of procedural training among the different rotations and hospitals. CONCLUSION: A handheld computer procedure logging system can be effectively used in a critical care training program. However, user acceptance was not uniform, and continued training and support are required to increase user acceptance. Such a procedure database may provide valuable information that may be used to optimize trainees' educational experience and to document clinical training experience for licensing and accreditation.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".