Proficiency of Medical Students at Obtaining Pressure Measurement Readings Using Automated Ankle and Toe Measuring Devices for Diagnosis of Lower Extremity Peripheral Artery Disease
Bibliographic record
Abstract
BACKGROUND: Pressure measurement is a key component in the diagnosis of lower extremity peripheral artery disease (PAD) but is technically challenging and time-consuming for nonvascular specialists, thus hindering its wider implementation. The aim of this study was to assess the proficiency of students at obtaining satisfactory ankle or toe pressure readings for PAD diagnosis using 2 automated devices. METHODS: Medical students followed a training session after which they performed ankle and toe pressure measurements to calculate the ankle-brachial index (ABI) using the MESI ABPI MD® device, and the toe-brachial index (TBI) using the SYSTOE® device. Blinded vascular specialists took the same measurements. Use of the automated devices was considered satisfactory when a valid reading was measured in as few attempts as possible. A comparison was made of each student's proficiency at performing valid ankle and toe pressure measurements. The secondary objective was to compare the readings taken by the vascular specialists with those of the students. RESULTS: Forty-three medical students were included. Mean number of attempts was 1.23 ± 0.48 with the MESI ABPI MD device and 1.44 ± 0.55 with the SYSTOE device (P = 0.04). There was no statistically significant difference between ABI readings taken by the students and those taken by the vascular specialists, 1.17 (0.90; 1.39) vs. 1.18 (0.86; 1.39) (P = 0.33), contrary to TBI readings 0.70 (0.22; 1.74) vs. 0.72 (0.23; 1.16) (P = 0.03). Measurement duration for the students and vascular specialists was 3.75 min ± 1.12 min and 2.26 min ± 0.82 min (P < 0.01) with the MESI ABPI MD device and 4.30 min ± 1.23 min and 3.33 min ± 1.49 min (P = 0.03) with the SYSTOE device. Correlation coefficients between the students and the vascular specialists were 0.56 and 0.34 with the MESI ABPI MD and SYSTOE devices (P < 0.05). CONCLUSIONS: After a brief theoretical training session, the medical students were better at taking ankle pressure measurements than toe pressure measurements with an automated device for the purposes of PAD diagnosis. It would be of value to assess the advantages of these automated devices in primary care practice in future research.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".