The role of serious concomitant injuries in the treatment and outcome of pediatric severe traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: The study objective was to describe the epidemiology of serious concomitant injuries and their effects on outcome in pediatric severe traumatic brain injury (sTBI). METHODS: A retrospective cohort of all severely injured (Injury Severity Score [ISS] ≥ 12) pediatric patients (<18 years) admitted to our pediatric intensive care unit, between 2000 and 2011, after experiencing an sTBI (Glasgow Coma Scale [GCS] score ≤ 8 and head Abbreviated Injury Scale [AIS] ≥ 4) were included. Two groups were compared based on the presence of serious concomitant injuries (maximum AIS score ≥ 3). Multivariate logistic regression was undertaken to determine variable associations with mortality. RESULTS: Of the 180 patients with sTBI, 113 (63%) sustained serious concomitant injuries. Chest was the most commonly injured extracranial body region (84%), with lung being the most often injured. Patients with serious concomitant injuries had increased age, weight, and injury severity (p < 0.001) and were more likely injured in a motor vehicle collision (91% vs. 48%, p < 0.001). Those with serious concomitant injuries had worse sTBI, based on lower presedation GCS (p = 0.031), higher frequency of fixed pupils (p = 0.006), and increased imaging abnormalities (SAH and DAI, p ≤ 0.01). Non-neurosurgical operations and blood transfusions were more frequent in the serious concomitant injury group (p < 0.01). The differences in mortality for the two groups failed to reach statistical significant (p = 0.053), but patients with serious concomitant injuries had higher rates of infection and acute central diabetes insipidus, fewer ventilator-free days, and greater length of stays (p < 0.05). Multivariate analyses revealed fixed pupillary response (odd ratio [OR], 63.58; p < 0.001), presedation motor GCS (OR, 0.23; p = 0.001), blood transfusion (OR, 5.80; p = 0.008), and hypotension (OR, 4.82; p = 0.025) were associated with mortality, but serious concomitant injuries was not (p = 0.283). CONCLUSION: Head injury is the most important prognostic factor in mortality for sTBI pediatric patients, but the presence of serious concomitant injuries does contribute to greater morbidity, including longer stays, more infections, fewer ventilator-free days, and a higher level of care required on discharge from hospital. LEVEL OF EVIDENCE: Prognostic and epidemiologic study, level III.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".