Vascular Anatomy of the Subacromial Space: A Map of Bleeding Points for the Arthroscopic Surgeon
Bibliographic record
Abstract
PURPOSE: Our purpose was to study the vascular anatomy of the subacromial space and to map the major sources of expected bleeding during subacromial arthroscopy surgery. METHODS: Ten shoulders of five adult cadavers underwent whole-body arterial perfusion with a mixture of lead oxide, gelatin, and water. The tissue specimens were dissected, photographed, radiographed, scanned, and analyzed with a digital software analyzer. Dissection of the arteries of the subacromial space, with their respective anatomic landmarks, was documented. Correlations of bleeding areas during subacromial arthroscopic surgery and cadaveric dissection were carried out. A vascular map of the walls of the subacromial space was created. RESULTS: The vascularity of the subacromial structures showed consistent patterns of distribution in 60% of the shoulders dissected, and specific sources of bleeding were analyzed. We divided this space into walls with their major arteries as follows: anterior wall, with the acromial branch of the thoracoacromial artery; posterior wall, with the posteromedial acromial branch of the suprascapular artery; and medial wall, with the anterior and posterior arteries of the acromioclavicular joint. The vascular map of the lateral wall, roof, and floor of the subacromial space was also described. CONCLUSIONS: Vascular maps of the arteries of the walls of the subacromial space were created. The subacromial space is highly vascular, and the pattern of blood supply was found to be constant in 60% of the shoulders dissected. This knowledge of the vascular anatomy may decrease bleeding during surgery. CLINICAL RELEVANCE: Knowledge of the vascular anatomy may decrease vascular damage during subacromial arthroscopy surgery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".