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Record W4322183409 · doi:10.55275/jposna-2023-602

Perioperative Blood Pressure Management for Patients Undergoing Spinal Fusion for Pediatric Spinal Deformity

2023· review· en· W4322183409 on OpenAlexaff
Nicholas D. Fletcher, Arvindera Ghag, Daniel Hedequist, Meghan N. Imrie, James T. Bennett, Michael P. Glotzbecker, Laurel C. Blakemore, Lorena V. Floccari, Megan Johnson, Selena Poon, Peter Sturm

Bibliographic record

VenueJournal of the Pediatric Orthopaedic Society of North America · 2023
Typereview
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicinePerioperativeSpinal deformitySpinal cordSurgerySpinal fusionDeformityAnesthesiaSpinal cord injuryGold standard (test)Radiology

Abstract

fetched live from OpenAlex

Posterior spinal instrumentation and fusion has become the gold standard for definitive management of children and adolescents with spinal deformity. Despite continued innovations designed to improve the safety profile of this complex surgical undertaking, spinal cord injury and resulting loss of neurologic function remain a rare but devastating risk. The increasing power of instrumentation combined with more aggressive correction strategies puts the spinal cord at particular risk due to traction. While the surgeon has the luxury of complex neuromonitoring techniques to alert the team in the presence of a neurologic change during surgery, maintenance of spinal cord perfusion throughout surgery and in the early postoperative period should be considered to avoid spinal cord ischemia as it accommodates to its new position after deformity correction. This manuscript represents recommendations of the POSNA Quality, Value, and Safety spine committee for optimization of blood pressure in the perioperative period.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.315
Teacher spread0.288 · 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
GenreReview

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

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Pediatric Orthopaedic Society of North AmericaSame topicIntraoperative Neuromonitoring and Anesthetic EffectsFrench-language works237,207