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Record W1990061613 · doi:10.14740/jnr.v4i1.269

A Review of Non-Invasive Methods of Monitoring Intracranial Pressure

2014· review· en· W1990061613 on OpenAlexvenueno aff
Derek Pobi Asiedu, Kyoung‐Jae Lee, Godfrey A. Mills, Elsie Effah Kaufmann

Bibliographic record

VenueJournal of Neurology Research · 2014
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntracranial pressureIntracranial pressure monitoringBiomedical engineeringIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

Intracranial p ressure (ICP) monitoring is an important aspect of neuro-medicine. ICP is the pressure created by the presence of cerebrospinal fluid. ICP monitoring techniques consist of invasive ( in vivo ) and non-invasive methods ( in vitro ). Modern research aims to eliminate invasive monitoring of ICP and promote non-invasive methods of monitoring ICP. This review aims to assess the current methods and research related to non-invasive monitoring of ICP. Invasive methods of monitoring ICP remain the most accurate methods of measuring ICP but are associated with many complications. This review discusses the current approaches, advantages and disadvantages of these approaches in ICP monitoring. A critical study of the literature review demonstrates that for accuracy and precision, an innovative non-invasive method for ICP monitoring is needed. There is more room for further research and development in ICP monitoring. A universal method of ICP monitoring is needed and not for a class or group of patients. J Neurol Res. 2014;4(1):1-6 doi: http://dx.doi.org/10.14740/jnr269w

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.189
GPT teacher head0.530
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Neurology ResearchSame topicTraumatic Brain Injury and Neurovascular DisturbancesFrench-language works237,207