Penetrating Abdominal Trauma: Descriptive Analysis of a Case Series from an Indian Metropolitan City
Bibliographic record
Abstract
Introduction Penetrating abdominal trauma (PAT) is a major injury that patients present to the emergency department in developed and developing countries. There are many modes and causes of injury. The aim of this study is to analyse the patterns of presentation and parameters at assessment, including investigations, interventions and outcomes of penetrating abdominal trauma at a major trauma centre in an Indian metropolitan city. Methods This is an observational descriptive study done over 18 months at a major trauma centre in a metropolitan city in India. The study was registered with the institutional ethics committee and the patients were recruited after obtaining consent on admission. The relevant details were collected from the patient's electronic records after admission and analysed. Results Stab wounds in the 21-40-year-old subset were the commonest. The small intestine was the most commonly injured organ. The mortality rates and the duration of the hospital stay were similar to other case series of the same condition. Conclusion The analysis of our case series has highlighted the patterns and outcomes of penetrating abdominal trauma in an urban demographic of a developing economy. Individuals in the prime of their lives, unfortunately, are victims of this mode of injury. Better implementation of standard management protocols can improve outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".