Wound Infection in Orthopaedic Surgery: A Cross Sectional Study at Tertiary Care Teaching Hospital in Dhaka
Bibliographic record
Abstract
Infections after operative procedures caused by multiple organisms appears with pain, fever; poor wound healing, antibiotic prolongation, need in-patient longer stays and increased expenses. It increases both morbidity and mortality. A cross-sectional descriptive study was conductedat Orthopaedics ward in Holy Family Red Crescent Medical College, a tertiary care teaching hospitalin Dhaka, Bangladesh for 3-month period to identify the frequently causative bacteria of wound infections and days of appearances of such infections. Tota1135 samples from patients with mean age of 35.77*I4.38 were analyzed. Patient history and clinical findings were collected in a data collection form during the study. Fifty-sixptis samples or wound swabs were collected from infected operated area and culture and biochemical tests for aerobic bacteria were done. Total of 21 from 36 samples were growth positive cultures (58.33%) and 15 were growth negative (41.66%). Most frequent organismcausing post-operative wound infection (POW!) was Pseudomonas aeruginosa,29.57% of positive isolatesandtheir post-operative days of appearances was mostly 6-10 days with82.7% frequencies. Surgical site infection is an unsettled ongoing problem which, although, cannot be completely rusticated.However, adequatepreventivestrategies against the most commonly isolated organism and proper care of wounds may reducethe occurrences of such infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".