Urazy głowy u dzieci – aktualne algorytmy diagnostyczno-lecznicze
Bibliographic record
Abstract
Head injuries in children are a common cause of consultation in emergency department. Glasgow Coma Scale (GSC) and, in case of infants, modified Glasgow Coma Scale are widely used for the evaluation of symptoms severity and divide head trauma into mild, moderate and severe. Guidelines concerning preliminary approach to mild head injury are based on the risk factors of intracranial injury. Risk of injury assessment criteria proposed by Polish Association of Paediatric Surgeons indicate the best places of medical consultation. Several guidelines concerning utility of head computed tomography (PECARN − Paediatric Emergency Care Applied Research Network, CHALICE − Children's Head Injury Algorithm for the Prediction of Important Clinical Events, CATCH − Canadian Assessment of Tomography for Childhood Head Injury) as well as time of patient observation and repetition of computed tomography scan were proposed. Special attention should be directed towards young athletes; in their case Child SCAT3 (Sport Concussion Assessment Tool 3) and SCAT3 are recommended. The goal of medical care of children with severe head trauma is mainly to eliminate secondary injury. Patients often require intensive monitoring and treatment of hypoxia, hypotension, hyperthermia and increased intracranial pressure. The clinicians should bear in mind the co-occurrence of spine trauma and post-traumatic seizures. Presented guidelines not only could influence patient care, but also decrease medical expenses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".