On the go with Toe & Flow: Private Clinic Design and Flow
Bibliographic record
Abstract
Lower extremity amputation secondary to diabetes and/or peripheral artery disease is a significant health issue globally. Many amputation prevention programs exist in academic settings; however, given the increasing administrative burdens associated with large institutions, it can be challenging to develop and maintain such programs in the modern era. Private amputation prevention clinics may be a viable alternative, allowing for greater control over services provided and better ability to meet patient needs. HOPE Vascular and Podiatry (https://hcic.io/), a private amputation prevention clinic established in 2023 in Houston, Texas, has successfully integrated a clinical, research, and educational program focused on amputation prevention. Key aspects of this program include a multidisciplinary team consisting of vascular/podiatric surgeons, clinical/administrative staff, and interdisciplinary collaborators. Notable advantages include decentralization of care, improvements in clinician satisfaction, and fewer administrative barriers to providing high-quality care. By identifying and quantifying the need for amputation prevention care in the community, establishing a clear mission, building a minimum viable program, and growing responsibly, there is potential to establish private amputation prevention clinics that provide high-quality, accessible, and personalized care for patients with diabetes and/or peripheral artery disease to improve limb outcomes. In this article, we describe the design and flow of HOPE Vascular and Podiatry, including how the clinic was developed, its mission and values, and ongoing clinical, research, and educational activities. We also share logistical, financial, and operational considerations, and provide lessons learned on how to effectively develop, maintain, and run a successful private amputation prevention clinic.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".