Bibliographic record
Abstract
Abdominal trauma is present in approximately 25% of pediatric patients with major trauma and is the most common cause of unrecognized fatal injury in children. Pediatric abdominal trauma is typically blunt in nature with the spleen being the most common organ injured. Nonoperative management is employed in over 95% of patients. Penetrating injuries are less common but often require operative management. Knowledge of specific mechanisms of injury aids the clinician in the diagnosis of specific injuries. Computed Tomography (CT) is the gold standard in the identification of intra-abdominal injury. Focused Assessment with Sonography for Trauma (FAST) can detect the presence of free fluid suggestive of intra-abdominal injury. In children, the utility of FAST is limited because less than half of pediatric patients with abdominal injury have free fluid. Bowel perforation and pancreatic injuries may not be evident on initial CT scanning of the abdomen. Initial management of the trauma patient in shock includes fluid boluses of normal saline or Ringer's lactate with two, large-bore upper extremity catheters. Transfusion with packed red blood cells is done if the patient remains hypotensive after the second fluid bolus. Emergent laparotomy is indicated in patients with: free intraperitoneal air, hemodynamic instability despite maximal resuscitative efforts (transfusion of greater than 50% of total blood volume), gunshot wound to the abdomen or other penetrating traumas, and evisceration of intraperitoneal contents. Initial FAST followed by abdominal computed tomography is important in the evaluation of the seriously or critically injured patient. The combination of the FAST exam along with selected abdominal computed tomography can further aid in the detection of injuries that may not be clinically apparent.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.012 |
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".