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Record W2780762050 · doi:10.5430/jnep.v8n5p44

Nursing care of the patient undergoing lumbar spinal fusion

2017· article· en· W2780762050 on OpenAlexvenueno aff
Maureen P. Lall

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLumbarPerioperative nursingSpinal fusionCauda equinaModalitiesSpinal stenosisPerioperativeLow back painNeurosurgerySurgerySpinal cordPathology

Abstract

fetched live from OpenAlex

Lumbar spinal fusion is a surgical procedure performed to join—or fuse—2 or more vertebrae in the low back. The procedure is done to stabilize the spine and prevent damage to the cauda equina and emanating nerve roots. Lumbar fusion is commonly indicated for patients with vertebral fractures, infection, or spinal tumors, and it may be appropriate for select patients with degenerative disorders and spinal stenosis. Nurses who care for patients undergoing lumbar fusion require an understanding of lumbar spinal anatomy, spinal pathology, surgical indications, and diagnostic modalities. Knowledge of the distinct surgical approaches and their respective advantages and disadvantages allows nurses to individualize patient care and be alert to postoperative complications. This article reviews clinical and research literature regarding lumbar fusion, with an emphasis on the role of the nurse in promoting a safe perioperative course.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.449
Teacher spread0.374 · 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".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

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