Angina on the Palm: randomized controlled pilot trial of Palm PDA software for referrals for cardiac testing.
Bibliographic record
Abstract
OBJECTIVE: Personal digital assistants (PDAs) are popular with physicians: in 2003, 33% of Canadian doctors reported using them in their practices. We do not know, however, whether using a PDA changes the behaviour of practising physicians. We studied the effectiveness of a PDA software application to help family physicians diagnose angina among patients with chest pain. DESIGN: Prospective randomized controlled pilot trial using a cluster design. SETTING: Primary care practices in the Toronto area. PARTICIPANTS: Eighteen family physicians belonging to the North Toronto Primary Care Research Network (Nortren) or recruited from a local hospital. INTERVENTIONS: We randomized physicians to receive a Palm PDA (which included the angina diagnosis software) or to continue conventional care. Physicians prospectively recorded the process of care for patients aged 30 to 75 presenting with suspected angina, over 7 months. MAIN OUTCOME MEASURES: Did the process of care for patients with suspected angina improve when their physicians had PDAs and software? The primary outcomes we looked at were frequency of cardiac stress test orders for suspected angina, and the appropriateness of referral for cardiac stress testing at presentation and for nuclear cardiology testing after cardiac stress testing. Secondary outcome was referrals to cardiologists. RESULTS: The software led to more overall use of cardiac stress testing (81% vs 50%). The absolute increase was 31% (P = .007, 95% confidence interval [CI] 8% to 58%). There was a trend toward more appropriate use of stress testing (48.6% with the PDA vs 28.6% control), an increase of 20% (P = .284, 95% CI -11.54% to 51.4%). There was also a trend toward more appropriate use of nuclear cardiology following cardiac stress testing (63.0% vs 45.5%), an absolute increase of 17.5% (P =.400, 95% CI -13.9% to 48.9%). Referrals to cardiologists did not increase (38.2% with the PDA vs 40.9%, P =.869). CONCLUSION: A PDA-based software application can lead to improved care for patients with suspected angina seen in family practices; this finding requires confirmation in a larger study.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".