Perceived Challenges to Routine Uptake of the Ankle Brachial Index within Primary Care Practice
Bibliographic record
Abstract
(1) Introduction: The ankle-brachial index (ABI) is the most widely used method of diagnosing peripheral arterial disease (PAD). However, the uptake of ABIs has been reported to be low in primary care settings across different various healthcare settings; however, this is yet to be investigated within the Canadian context. (2) Objective: Therefore, we sought to assess the rates of ABI usage as well as perceived barriers among primary care practitioners (PCPs) in Toronto, Canada. (3) Methods: A modified questionnaire was electronically sent to 257 PCPs in the Greater Toronto Area (GTA). Questions pertained to frequency, feasibility, utility, and barriers associated with ABI usage in clinical practice. Responses were collected and tallied. (4) Results: A total of 52 PCPs completed the questionnaire. 79% of PCPs did not routinely perform ABIs within their clinical practice, and 56% deemed ABI usage as unfeasible. Constraints in time and staff personnel, as well as complexity of ABI result interpretation, were cited as the major perceived barriers to ABI usage. The overwhelming majority of PCPs viewed alternative forms of diagnosis, such as a blood test for PAD, as being preferable to ABI, as such an approach would enhance diagnostic simplicity and efficiency. (5) Conclusion: ABI usage rates are poor within primary care practices in Toronto, Canada. Alternative approaches for diagnosing PAD may result in greater adoption rates among PCPs and therefore improve the identification of patients with PAD.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".