A study of wound infections and its antibiogram in surgical intensive care unit of a tertiary care hospital
Bibliographic record
Abstract
Background: Wound infections can be caused through two major sources: exogenous and endogenous bacteria. The probability of wound infection largely depends on the patients systemic host defenses, local wound conditions and microbial burden. Despite modern surgical techniques and the use of antibiotic prophylaxis, Surgical Site Infection (SSI) is one of the most common complications encountered in surgery. SSI places a significant burden on both the patient and health system. SSI is thus a major cause of morbidity, prolonged hospital stay and increased health costs. Objective of this study was to identify and isolate various bacteria from wound infections in a surgical intensive care unit and to study their antibiogram. Methods: Two wound swabs were collected from the wound and from a drop of aspirate, smear was made on clean glass slide and Gram staining was done for direct microscopic examination under oil immersion 100X objective to know various morphological types of bacteria and presence or absence of inflammatory cells. Second swab/drop of aspirate was used for culture by inoculating it on routine media like Blood Agar, Nutrient Agar and Mac Conkeys agar, incubated at 37° C for 24 hours aerobically. Result: Out of 238 pus samples, 209 (88.6%) were culture positive for bacterial growth and no growth was observed in 29(12.4%) cases. Out of 209 bacterial culture positive cases, 201 were monobacterial and 8 were poly bacterial. Out of 201 bacterial isolates; S. aureus (56/26.6) was the commonest followed by P. aeruginosa (47/22.4%). Conclusion: The study concludes that variety of aerobic bacteria is responsible for wound infection with predominance of Staphylococcus aureus followed by Pseudomonas aeruginosa
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".