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Record W2198639225 · doi:10.1097/pec.0b013e318243fb72

Emergency Management of Increased Intracranial Pressure

2012· review· en· W2198639225 on OpenAlexaff
Sandy Pitfield, Allison Carroll, Niranjan Kissoon

Bibliographic record

VenuePediatric Emergency Care · 2012
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineIntracranial pressureCerebral perfusion pressureTraumatic brain injuryEtiologyEmergency departmentIntensive care medicineIntensive careLimitingIntracranial pressure monitoringRefractory (planetary science)PerfusionAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Primary neurological injury in children can be induced by diverse intrinsic and extrinsic factors including brain trauma, tumors, and intracranial infections. Regardless of etiology, increased intracranial pressure (ICP) as a result of the primary injury or delays in treatment may lead to secondary (preventable) brain injury. Therefore, early diagnosis and aggressive treatment of increased ICP is vital in preventing or limiting secondary brain injury in children with a neurological insult. Present management strategies to improve survival and neurological outcome focus on reducing ICP while optimizing cerebral perfusion and meeting cerebral metabolic demands. Targeted therapies for increased ICP must be considered and implemented as early as possible during and after the initial stabilization of the child. Thus, the emergency physician has a critical role to play in early identification and treatment of increased ICP. This article intends to identify those patients at risk of intracranial hypertension and present a framework for the emergency department investigation and treatment, in keeping with contemporary guidelines. Intensive care management and the treatment of refractory increases in ICP are also outlined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.039
GPT teacher head0.321
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2012
Admission routes1
Has abstractyes

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