Incidence of infection following internal fixation of open and closed tibia fractures in India (INFINITI): a multi-centre observational cohort study
Bibliographic record
Abstract
BACKGROUND: Trauma is a major public health problem, particularly in India due to the country's rapid urbanization. Tibia fractures are a common and often complicated injury that is at risk of infection following surgical fixation. The primary objectives of this cohort study were to determine the incidence of infection within one year of surgery and to describe the distribution of infections by location and time of diagnosis for tibia fractures in India. METHODS: We conducted a multi-center, prospective cohort study. Patients who presented with an open or closed tibia fracture treated with internal fixation to one of the participating hospitals in India were invited to participate in the study. Participants attended follow-up visits at 3, 6, and 12 months post-surgery, where they were assessed for infections, fracture healing, and health-related quality of life as measured by the EurQol-5 Dimensions (EQ-5D). RESULTS: Seven hundred eighty-seven participants were included in the study and 768 participants completed the 12 month follow-up. The overall incidence of infection was 2.9% (23 infections). The incidence of infection was 1.6% (10 infections) in closed and 8.0% (13 infections) in open fractures. There were 7 deep and 16 superficial infections, with 5 being early, 7 being delayed, and 11 being late infections. Intra-operative antibiotics were given to 92.1% of participants and post-operative antibiotics were given to 96.8% of participants. Antibiotics were prescribed for an average of 8.3 days for closed fractures and 9.1 days for open fractures. Infected fractures took significantly longer to heal, and participants who had an infection had significantly lower EQ-5D scores. CONCLUSIONS: The incidence of infection within this cohort is similar to those seen in developed countries. The duration of prophylactic antibiotic use was longer than standard practice in North America, raising concern for the potential development of antibiotic resistant microbes within Indian orthopaedic settings. Future research should aim to identify the best practice for antibiotic use in India to ensure that antibiotic usage patterns do not lead to unnecessary overuse, while maintaining a low incidence of infection. TRIAL REGISTRATION: NCT01691599 , September 17, 2012.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".