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Record W1544118336 · doi:10.3171/2014.4.jns13932

Diagnostic accuracy of intraocular pressure measurement for the detection of raised intracranial pressure: meta-analysis

2014· review· en· W1544118336 on OpenAlexafffund
Daniel Yavin, Judy Luu, Matthew T. James, Derek J. Roberts, Garnette R. Sutherland, Nathalie Jetté, Samuel Wiebe

Bibliographic record

VenueJournal of neurosurgery · 2014
Typereview
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsMedicineIntraocular pressureMeta-analysisOcular hypertensionIntracranial pressureOphthalmologyInternal medicineLikelihood ratios in diagnostic testingMEDLINESurgery

Abstract

fetched live from OpenAlex

OBJECT: Because clinical examination and imaging may be unreliable indicators of intracranial hypertension, intraocular pressure (IOP) measurement has been proposed as a noninvasive method of diagnosis. The authors conducted a systematic review and meta-analysis to determine the correlation between IOP and intracranial pressure (ICP) and the diagnostic accuracy of IOP measurement for detection of intracranial hypertension. METHODS: The authors searched bibliographic databases (Ovid MEDLINE, Ovid EMBASE, and the Cochrane Central Register of Controlled Trials) from 1950 to March 2013, references of included studies, and conference abstracts for studies comparing IOP and invasive ICP measurement. Two independent reviewers screened abstracts, reviewed full-text articles, and extracted data. Correlation coefficients, sensitivity, specificity, and positive and negative likelihood ratios were calculated using DerSimonian and Laird methods and bivariate random effects models. The I(2) statistic was used as a measure of heterogeneity. RESULTS: Among 355 identified citations, 12 studies that enrolled 546 patients were included in the meta-analysis. The pooled correlation coefficient between IOP and ICP was 0.44 (95% CI 0.26-0.63, I(2) = 97.7%, p < 0.001). The summary sensitivity and specificity for IOP for diagnosing intracranial hypertension were 81% (95% CI 26%-98%, I(2) = 95.2%, p < 0.01) and 95% (95% CI 43%-100%, I(2) = 97.7%, p < 0.01), respectively. The summary positive and negative likelihood ratios were 14.8 (95% CI 0.5-417.7) and 0.2 (95% CI 0.02-1.7), respectively. When ICP and IOP measurements were taken within 1 hour of another, correlation between the measures improved. CONCLUSIONS: Although a modest aggregate correlation was found between IOP and ICP, the pooled diagnostic accuracy suggests that IOP measurement may be of clinical utility in the detection of intracranial hypertension. Given the significant heterogeneity between included studies, further investigation is required prior to the adoption of IOP in the evaluation of intracranial hypertension into routine practice.

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)0.034
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.100
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0220.065
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.132
GPT teacher head0.345
Teacher spread0.213 · 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 designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations29
Published2014
Admission routes2
Has abstractyes

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