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Record W2940735429 · doi:10.1055/s-0039-1685

Minimally Invasive Craniocervical Decompression for Chiari 1 Malformation: An Operative Technique

2019· article· en· W2940735429 on OpenAlexaff
Javier Quillo-Olvera, Rodrigo Navarro-Ramírez, Diego Quillo-Olvera, Javier Quillo-Reséndiz, Jin‐Sung Kim

Bibliographic record

VenueJournal of Neurological Surgery Part A Central European Neurosurgery · 2019
Typearticle
Languageen
FieldMedicine
TopicSpinal Dysraphism and Malformations
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineForamen magnumDecompressionSurgeryChiari malformationRetractorOccipital boneSyringomyeliaMagnetic resonance imagingSkullRadiology

Abstract

fetched live from OpenAlex

Chiari malformation type 1 (CM-1) is an ectopia of the cerebellar tonsils below the foramen magnum that causes severe disability due to its neurologic symptoms. The treatment of choice for CM-1 is decompression of the craniovertebral junction (CVJ). In some patients only an extradural decompression by removing the atlanto-occipital ligament may be sufficient. In other patients, duraplasty is necessary. In this case, we report the operative technique used to treat a CM-1 in a 16-year-old male patient who presented with severe headache and gait instability. A micro-decompression of the suboccipital bone and posterior arch osteotomy of C1 through a 2-cm midline incision was performed under surgical microscope magnification. A duraplasty was performed through the same approach. The patient was discharged home after 2 days in the hospital and returned to regular activities at school 3 weeks after surgery. The minimally invasive technique presented here is a viable option for the posterior decompression of the CVJ in patients with CM-1 using a low-cost self-retaining retractor.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.279
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Neurological Surgery Part A Central European NeurosurgerySame topicSpinal Dysraphism and MalformationsFrench-language works237,207