The role of directional atherectomy in critical-limb ischemia
Bibliographic record
Abstract
BACKGROUND: Our aim was to review the current literature of the use of directional atherectomy (DA) in the treatment of lower extremity critical-limb ischemia. METHODS: A search for relevant literature was performed in PubMed and PubMed Central on 16 April 2020, sorted by best match. Three searches across two databases were performed. Articles were included that contained clinical and procedural data of DA interventions in lower extremity critical-limb ischemia patients. All studies that were systematic reviews were excluded. RESULTS: Eleven papers were included in this review. Papers were examined under several parameters: primary patency and secondary patency, limb salvage/amputation, technical/procedural success, complications/periprocedural events, and mean lesion length. Primary and secondary patency rates ranged from 56.3% to 95.0% and 76.4% to 100%, respectively. Limb salvage rates ranged from 69% to 100%. Lesion lengths were highly varied, representing a broad population, ranging from 30 ± 33 mm to 142.4 ± 107.9 mm. CONCLUSIONS: DA may be a useful tool in the treatment of lower extremity critical-limb ischemia.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| 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".