Survey of Personal Digital Assistant Use in Radiology
Bibliographic record
Abstract
There has been widespread adoption of personal digital assistants (PDAs) within medicine in recent years. However, information on the prevalence and usage of these devices among radiologists is limited. A survey was designed and mailed to randomly selected members of the Radiological Society of North America to determine the percentage of PDA users, their use patterns, and the types of applications that they would like to see in the future. The use patterns of attending radiologists were compared with those of trainees (residents and fellows). Overall usage was also compared with the relevant findings in two surveys of internal medicine users. It was found that slightly less than one-half of respondents used PDAs on a daily basis, a finding that was comparable to that in the internal medicine surveys. However, less than one-quarter of PDA users had radiology-specific applications installed on their devices, whereas a greater percentage of internal medicine users had software such as drug databases and clinical references on their PDAs. Radiology trainees had a higher rate of both PDA ownership and radiology application usage than did attending radiologists. It is likely that, as PDA hardware becomes more powerful, with higher display resolution, better wireless networking capabilities, and greater memory capacity, PDA ownership as well as radiology application usage will increase.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".