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Record W2985439357 · doi:10.1016/j.avsg.2019.10.092

Proficiency of Medical Students at Obtaining Pressure Measurement Readings Using Automated Ankle and Toe Measuring Devices for Diagnosis of Lower Extremity Peripheral Artery Disease

2019· article· en· W2985439357 on OpenAlexfundno aff
Floriane Catillon, Stéphane Tuffier, Antoine Guilcher, Quentin Tollenaere, Antoine Métairie, Annaïg Miossec, Chadi Mauger, Damien Lanéelle, Guillaume Mahé

Bibliographic record

VenueAnnals of Vascular Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
FundersMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsMedicineArterial diseaseAnklePeripheralDiseaseRadiologyCardiologyInternal medicineSurgeryVascular disease

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.004

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.

Opus teacher head0.084
GPT teacher head0.333
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations6
Published2019
Admission routes1
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