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Record W2782218401 · doi:10.5435/jaaos-d-16-00184

Blood-loss Management in Spine Surgery

2018· review· en· W2782218401 on OpenAlexaff
Jesse E. Bible, Muhammad Z. Mirza, Mark A. Knaub

Bibliographic record

VenueJournal of the American Academy of Orthopaedic Surgeons · 2018
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsObject Research Systems (Canada)
Fundersnot available
KeywordsMedicineBlood lossBlood managementHemostasisPerioperativeSurgeryAnesthesiaBlood transfusionTranexamic acidAntifibrinolyticHemostatic AgentIntensive care medicine

Abstract

fetched live from OpenAlex

Substantial blood loss during spine surgery can result in increased patient morbidity and mortality. Proper preoperative planning and communication with the patient, anesthesia team, and operating room staff can lessen perioperative blood loss. Advances in intraoperative antifibrinolytic agents and modified anesthesia techniques have shown promising results in safely reducing blood loss. The surgeon's attention to intraoperative hemostasis and the concurrent use of local hemostatic agents also can lessen intraoperative bleeding. Conversely, the use of intraoperative blood salvage has come into question, both for its potential inability to reduce the need for allogeneic transfusions as well as its cost-effectiveness. Allogeneic blood transfusion is associated with elevated risks, including surgical site infection. Thus, desirable transfusion thresholds should remain restrictive.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.341
Teacher spread0.297 · 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 teacher head, not a consensus.

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

Citations79
Published2018
Admission routes1
Has abstractyes

Explore more

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