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Record W4406645552 · doi:10.1097/ogx.0000000000001358

Preoperative Anemia Prior to Gynecologic Surgery Is Associated With Increased Healthcare Costs

2025· article· en· W4406645552 on OpenAlexaff
Ally Murji, Melody Lam, Lindsay Shirreff, Lorraine L. Lipscombe, Wanrudee Isaranuwatchai

Bibliographic record

VenueObstetrical & Gynecological Survey · 2025
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsInstitute of Health Services and Policy ResearchWomen's College HospitalInstitute for Work & HealthMount Sinai Hospital
Fundersnot available
KeywordsMedicineAnemiaHealth careGynecologic cancerGeneral surgeryGynecologic surgical proceduresLaparoscopyInternal medicineCancerOvarian cancer

Abstract

fetched live from OpenAlex

(Abstracted from J Minim Invasiv Gynecol 2024;31:778.e1–786.e1 Current guidelines state that preoperative anemia is a risk factor for adverse outcomes such as perioperative blood transfusion, surgical site infection, prolonged hospital stay, and hospital readmission after myomectomy or hysterectomy. Though standardized guidance states that this should be addressed prior to surgical procedures, it has been reported that as many as 1 in 5 patients having hysterectomies or myomectomies for benign causes is anemic at the time of their procedure.

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)0.002
metaresearch head score (Gemma)0.085
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.085
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.044
GPT teacher head0.324
Teacher spread0.280 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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