Self-Reported Diagnosis and Management of Surgical Site Infection Highlights Lack of Objective Measures and Treatment Guidance
Bibliographic record
Abstract
Background: There is little guidance regarding empiric therapy for superficial surgical site infections (SSIs). Management of incisions with signs of SSI lacks consensus and management is variable among individual surgeons. Methods: The Surgical Infection Society was surveyed regarding management of SSIs. Cases were provided with varying wound descriptions, initial wound class (WC), post-operative day, and presence of a prosthesis. Responses were in multiple-choice format; statistics: χ 2 ; α = 0.05. Results: Seventy-eight members responded. For appearance scenarios, respondents believed that both mild erythema (55%) and clear drainage (64%) could be observed, whereas substantial (>3 cm) erythema or purulence should be treated with complete (22% and 50%) or partial (55% and 40%) opening of the incision. Degree of erythema did not influence administration of antibiotic agents, but purulence was more likely than clear drainage to be treated with antibiotics (38% vs. 6%; p < 0.001). There were no differences based on WC, except that clean cases were more likely than higher WC scenarios to be treated with gram-positive coverage alone (WC 1 [26%] vs. 2 [10%] vs. 3 [13%] vs. 4 [4%]; p < 0.001). Post-operative day (POD) three appeared to be an inflection point for more aggressive treatment of suspected incisional SSI, with fewer (POD 0 [86%] vs. POD day 3 [54%]; p < 0.001) reporting observation. Respondents were more likely to obtain imaging, start broad-spectrum antibiotic agents, and return to the operating room for purulence in the presence of a mesh. Conclusions: Presented with escalating possibility of SSI, respondents reported lower rates of observation, increased use of antibiotic agents, and increased surgical drainage. Many scenarios lack consensus regarding appropriate therapy. The complete elimination of SSIs is unlikely to be accomplished soon, and this study provides a framework for understanding how surgeons approach SSIs, and potential areas for further research or pragmatic guidance.
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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 | 0.008 | 0.041 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".