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Record W2927829144 · doi:10.4103/1673-5374.253513

The “Brain Stress Timing” phenomenon and other misinterpretations of randomized clinical trial on aneurysmal subarachnoid hemorrhage

2019· review· en· W2927829144 on OpenAlexaff
Rafael Martínez-Pérez, Natalia Rayo, Agustín Montivero, JorgeMarcelo Mura

Bibliographic record

VenueNeural Regeneration Research · 2019
Typereview
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsWestern University
Fundersnot available
KeywordsSubarachnoid hemorrhageClipping (morphology)Endovascular coilingMedicineAneurysmRandomized controlled trialIntensive care medicineEndovascular treatmentSurgery

Abstract

fetched live from OpenAlex

Clipping and coiling are currently the two alternatives in treatment of ruptured cerebral aneurysms. In spite of some meritorious analysis, further discussion is helpful to understand the actual state of art. Retreatment and rebleeding rates clearly favors clipping, although short-term functional outcome seems to be beneficial for clipping, while this different is not such if we perform the comparison at a longer follow up. Long-term follow ups and cost analysis are mandatory to have a clear view of the current picture in treatment of subarachnoid hemorrhage. Treatment strategy should be made by a multi-disciplinary team in accredited centers with proficient experience in both techniques.

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)0.023
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.086
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.002
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 head0.246
GPT teacher head0.480
Teacher spread0.234 · 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.

Study designNot applicable
DomainMethods
GenreCommentary

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
Published2019
Admission routes1
Has abstractyes

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