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Record W3141953003 · doi:10.1093/neuonc/noab035

The relevance of surgical status in nodular leptomeningeal metastasis patient outcomes

2021· letter· en· W3141953003 on OpenAlexaff
Matthew Dankner, Sarah M. Maritan

Bibliographic record

VenueNeuro-Oncology · 2021
Typeletter
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsMcGill UniversityOccupational Cancer Research Centre
Fundersnot available
KeywordsMedicineRelevance (law)MetastasisClinical significanceRadiologyPathologyInternal medicineCancer

Abstract

fetched live from OpenAlex

We read with great interest the publication “Prognostic validation and clinical implications of the EANO ESMO classification of leptomeningeal metastasis from solid tumors.” 1 This study corroborates new guidelines pertaining to the management of leptomeningeal metastases (LM) by applying a novel LM patient cohort that reveals interesting correlations between LM type and outcome. In their analysis of type II LM, defined as LM without positive cerebrospinal fluid cytology but with typical clinical and MRI signs, the authors identify LM with nodular MRI pattern as a poor prognostic indicator. Several previously published studies demonstrate that patients treated with surgery and stereotactic radiosurgery (SRS) for parenchymal brain metastases develop nodular leptomeningeal lesions.2–4 The authors of these studies note that nodular LM in the postoperative setting is associated with favorable prognosis compared to linear LM.2,4 Given the apparent discrepancy between the findings of previous studies and the publication discussed herein, we request clarification from the authors on the treatment status of the patients with type II nodular LM described in the study. What percentage of patients with type II nodular LM had previous surgical resection and/or SRS in proximity to the newly developed nodular LM? Do nodular LM patients with previous surgery and/or SRS experience differential prognosis? The field of LM is rapidly evolving to define subsets of patients with differential outcomes that will play important roles in future iterations of clinical guidelines and clinical trial development. Recent studies have elucidated growth patterns of brain metastasis invasion,5 MRI pattern,1–4 and treatment status1,2,4 as features associated with LM patient outcome.6 Given the convincing relevance of nodular vs linear LM as prognostic imaging features, it is critical to comprehensively define the context of treatment status contributing to LM phenotypes and outcomes. We congratulate the authors on advancing these concepts forward for the benefit of LM patients and look forward to their reply.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.311
Teacher spread0.285 · 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 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".

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Citations0
Published2021
Admission routes1
Has abstractno

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