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Record W4408432972 · doi:10.1016/j.jogc.2025.102818

Revisiting Routine: Reducing Unnecessary CBC Testing After Scheduled Cesarean Delivery

2025· article· en· W4408432972 on OpenAlexaffvenue
Yannay Khaikin, Wei‐Hsuan Yu

Bibliographic record

VenueJournal of Obstetrics and Gynaecology Canada · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineCesarean deliveryObstetricsMedical emergencyPregnancyIntensive care medicine

Abstract

fetched live from OpenAlex

Patients admitted to hospital undergo unnecessary blood work, leading to patient harm, additional testing, health care inefficiency, and environmental waste. Many hospitals routinely order a complete blood count (CBC) after cesarean delivery to detect significant anemia. The argument against routine postoperative testing is threefold: (1) routine hemoglobin measurement does not change clinical management for most patients and can cause harm; (2) risk factors for developing anemia that requires transfusion are known (e.g., preoperative anemia, high intraoperative blood loss, and non-elective surgery), and (3) postpartum bedside assessment can trigger a CBC draw for patients with symptoms (e.g., dizziness, oliguria, tachycardia, and hypotension).

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)0.005
metaresearch head score (Gemma)0.059
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.059
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

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