Practical Review on Delayed Primary Closure: Basic Science and Clinical Applications
Bibliographic record
Abstract
Summary: Wound healing complications present a significant burden on both patients and health-care systems, and understanding wound healing principles is crucial across medical and surgical specialties to help mitigate such complications. One of these longstanding principles, specifically delayed primary closure (DPC), described as mechanically closing a wound after several days of secondary intention healing, lacks clear consensus on its definition, indications, and outcomes. This practical review examines wound healing fundamentals, focusing on DPC, its execution, indications, and comparative outcomes. A PubMed literature search was conducted to retrieve studies on DPC. Inclusion criteria included comparative studies assessing outcomes and complications between DPC and other closure techniques, as well as articles investigating DPC’s underlying physiology. Twenty-three comparative studies met inclusion criteria. DPC wounds have significantly higher partial pressure of oxygen, higher blood flow, and higher rates of collagen synthesis and remodeling activity, all of which help explain DPC wounds’ superior mechanical strength. DPC seems most beneficial in contaminated wounds, such as complicated appendectomies, postcardiac surgery wounds, and complicated abdominal wall reconstructions, where it has been associated with lower rates of surgical site infections. This practical review provides an evidence-based approach to DPC, its physiology, technique, and indications. Based on the existing literature, the authors recommend that DPC wounds should be dressed in saline/betadine soaks, changed and irrigated daily, with delayed closure lasting between 3 and 5 days or until the infection has resolved. The clearest indications for DPC are in the context of contaminated abdominal surgery and sternal wound dehiscence post cardiac surgery.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".