Propeller DICAP flap for a large defect on the back—Case report and review of the literature
Bibliographic record
Abstract
Reconstruction of large soft tissue defects of the back is a challenging problem. Large defects of the back were reconstructed with multiple random pattern or local pedicled muscle (and skin graft) or musculocutaneous flaps. The clinical use of perforator flaps has demonstrated that harvesting of flaps on a single perforator is possible for reconstruction of large defects. We present a 71-year-old male with a lesion on his left mid back that measured 10 × 10 × 4 cm(3) . Biopsy of the lesion was consistent with dermatofibrosarcoma protruberans. Wide local excision of the lesion with 4 cm margin was performed. The soft tissue defect, ~20 cm in diameter, was reconstructed with a large propeller dorsal intercostal artery perforator (DICAP) flap. The DICAP flap measured 40 × 15 cm(2) based on a single perforator-lateral branch of dorsal rami of the seventh posterior intercostal artery on the right side. The perforator flap was elevated at the subfascial level and transposed 180° into the defect. The donor site on the right side of the back was closed directly. This case illustrates the size of the propeller DICAP flap that could be safely harvested on a single perforator from the dorsal rami of the posterior intercostal artery. To our knowledge this is the largest reported pedicled perforator flap harvested on a single perforator on the posterior trunk.
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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".