Intracranial pressure monitoring in children with severe traumatic brain injury: A retrospective study
Bibliographic record
Abstract
Introduction: There is a paucity of literature on intracranial pressure (ICP) monitoring in children. The aim of this study was to ascertain whether ICP monitoring is useful in children with severe traumatic brain injury (TBI). Materials and Methods: Medical records of children between 1 and 12 years, admitted to neurocritical care unit with severe TBI in 2 years, were reviewed. The children were divided into two groups: study group (ICP monitored) and control group (ICP not monitored). Admission demographics, vital parameters, and computed tomographic scan findings were recorded. In the study group, date of ICP catheter insertion/removal with ICP values and treatment carried out for increased ICP were noted. Data on tracheostomy, duration of mechanical ventilation, hospital stay, and outcome at discharge were noted. Results: Demographic variables were comparable between the two groups. When adjusted for death, no significant difference was observed between the study and the control groups in median duration of mechanical ventilation: 35 days (95% confidence interval [CI]: 12–73) versus 55 days (95% CI: 29–55) (P = 0.96), hospital stay: 36 days (95% CI: 12–73) versus 58 days (95% CI: 29–58) (P = 0.96), and time to tracheostomy: 6 days (95% CI: 5–8) versus 5 days (95% CI: 4–7) (P = 0.49). Mortality rates, incidence of cranial surgeries, and outcome at discharge were also comparable. Conclusion: ICP monitoring did not reduce the incidence of death, cranial surgeries, duration of mechanical ventilation, hospital stay, or improve the outcome at discharge in children with severe TBI.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".