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Record W4413190690 · doi:10.2478/rjr-2025-0019

Usefulness of topical intranasal fluorescein for localization of anterior skull base fluid fistulas: a systematic review

2025· article· en· W4413190690 on OpenAlexaboutno aff
Marelyn Medina, Juan Antonio Lugo Machado, Diana Isabel Espinoza Morales, Araceli Zazueta Cárdenas

Bibliographic record

VenueRomanian Journal of Rhinology · 2025
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsnot available
FundersUniversidad de Sonora
KeywordsSkullMedicineNasal administrationFluoresceinSurgeryOpticsPhysicsFluorescence

Abstract

fetched live from OpenAlex

Abstract OBJECTIVE. This study evaluates the available evidence on the usefulness of topical intranasal fluorescein in diagnosing and localizing nasal cerebrospinal fluid (CSF) leakage. MATERIAL AND METHODS. A systematic review of the literature was conducted to investigate the usefulness and safety of intranasal topical fluorescein for locating nasal cerebrospinal fluid fistulas, following PRISMA guidelines. Articles were searched in databases including Scopus, PubMed, and ScienceDirect, (“Intranasal Fluorescein” AND “Cerebrospinal Fluid Fistula”) OR (“Intranasal Fluorescein” AND “CSF Fistula”) in the publications with no time restriction, in the English language. The chosen articles were evaluated with the Newcastle-Ottawa Scale. A table was used to summarize the results. RESULTS. The 6 included studies on topical intranasal fluorescein to locate CSF fistulas show high effectiveness and safety, with success rates close to 100%. However, limitations include small samples, lack of control groups, and the need for more comparative studies. The methodological quality of the studies is mostly moderate. CONCLUSION. Intranasal topical fluorescein is an effective and safe method to locate intranasal cerebrospinal fluid leaks, with high diagnostic accuracy. It is a less invasive and more economical option compared to techniques such as intrathecal fluorescein. More controlled research is needed to confirm its effectiveness.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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