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Record W4417251939 · doi:10.1007/s00701-025-06734-x

Influence of dural attachment on remnant progression after subtotal resection in WHO grade 1 meningioma

2025· article· en· W4417251939 on OpenAlexaff
Marc-Olivier Comeau, F. Sánchez Gascón, Mégan Corbeil, Xavier Roberge, Martin Côté, Guilherme Gago, Pierre‐Olivier Champagne

Bibliographic record

VenueActa Neurochirurgica · 2025
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMeningiomaNeuroradiologyDura materNeurosurgeryInterventional radiologyNeurologyResection

Abstract

fetched live from OpenAlex

PURPOSE: Meningioma resection remains partial in over 25% of cases, with few known predictors of residual disease progression. This study aims to assess if dural attachment, identified on postoperative imaging, influences meningioma progression following subtotal resection. METHODS: A retrospective cohort design was applied to patients who underwent subtotal meningioma resection at our institution. MRI data collected over time included remnant volumes, surface areas of contact with dura and signal intensity ratios. Progression was defined as an increase in remnant volume of at least 25%. RESULTS: , respectively. Criteria for progression was met in 35 remnants (57.4%), with a mean time to progression of 18.1 months. Greater surface area of contact with dura relative to total remnant surface area was a significant predictor of progression in both univariate and multivariate analyses. Smaller remnant diameter and larger extent of resection were marginally associated with progression. Signal intensity ratios failed to demonstrate association with progression. CONCLUSION: The degree of meningioma remnant attachment to dura appears associated with progression. Reduction of dural involvement in residual disease intentionally left in place may be considered when appropriate, with further studies needed to refine surgical considerations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.304
Teacher spread0.292 · 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 designObservational
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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