Wound Closure and Wound Dressings in Adult Spinal Deformity Surgery From the AO Spine Surveillance of Post-Operative Management
Bibliographic record
Abstract
STUDY DESIGN: An e-mail-based online survey for adult spinal deformity (ASD) surgeons. OBJECTIVE: Wound closure and dressing techniques may vary according to the discretion of the surgeon as well as geographical location. However, there are no reports on most common methods. The purpose of this study is to clarify the consensus. METHODS: An online survey was distributed via email to AO Spine members. Responses from 164 ASD surgeons were surveyed. The regions were divided into 5 regions: Europe and South Africa (ESA), North America (NA), Asia Pacific (AP), Latin America (LA), and Middle East and North Africa (MENA). Wound closure methods were evaluated by glue(G), staples(S), external non-absorbable sutures (ENS), tapes(T), and only subcuticular absorbable suture (SAS). Wound Dressings consisted of dry dressing (D), plastic occlusive dressing (PO), G, Dermabond Prineo (DP). RESULTS: The number of respondents were 57 in ESA, 33 in NA, 36 in AP, 22 in LA, and 16 in MENA. S (36.4%) was the most used wound closure method. This was followed by ENS (26.2%), SAS (14.4%), G (11.8%), and T (11.3%). S use was highest in ESA (44.3%), NA (28.6%), AP (31.7%), and MENA (58.8%). D was used by 50% of surgeons postoperatively. AP were most likely to use PO (36%). 21% of NA used DP, while between 0%-9% of surgeons used it in the rest of the world. CONCLUSION: Wound closure and dressings methods differ in the region. There are no current guidelines with these choices. Future studies should seek to standardize these choices.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".