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Record W3211043776 · doi:10.1093/pch/pxab061.121

153 Cost of Hospitalization in Infants with Hypoxic Ischemic Encephalopathy Treated with Therapeutic Hypothermia in a Quebec Tertiary Neonatal Intensive Care Unit and Validation of the Canadian Neonatal Network Costing Algorithm

2021· article· en· W3211043776 on OpenAlexaffabout
Wijdan Basfar, Elias Jabbour

Bibliographic record

VenuePaediatrics & Child Health · 2021
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineActivity-based costingHypoxic Ischemic EncephalopathyNeonatal intensive care unitPediatricsPsychological interventionAlgorithmEncephalopathyGestational ageEmergency medicinePregnancyInternal medicineComputer science

Abstract

fetched live from OpenAlex

Abstract Primary Subject area Neonatal-Perinatal Medicine Background Therapeutic hypothermia (TH) is the standard treatment for neonatal hypoxic ischemic encephalopathy (HIE) to improve mortality and long-term impairment. Accurate costing algorithms are essential to evaluate cost-effective interventions and identify cost drivers. Objectives We aimed to validate the Canadian Neonatal Network (CNN) costing algorithm for HIE infants treated with TH against costs obtained from hospital-based finance software (CPSS) and compare the costs of TH for infants with mild/moderate to those with severe HIE. We aimed to validate the Canadian Neonatal Network (CNN) costing algorithm for HIE infants treated with TH against costs obtained from hospital-based finance software (CPSS) and compare the costs of TH for infants with mild/moderate to those with severe HIE. Design/Methods Retrospective cohort study including 98 infants admitted with HIE and receiving TH in a tertiary NICU between 2016 and 2018. Clinical characteristics and CNN costing data were collected from the local CNN database and actual cost were obtained from CPSS. The primary outcome was the difference in total hospital stay cost between CNN algorithm and CPSS. The differences between both algorithms were also identified in 8 different cost centres such as nursing, respiratory, imaging, etc. Costs per patients using both algorithms were compared using Pearson correlation coefficient (r) and paired t-test. Characteristics and costs per infant were compared between infants with mild/moderate HIE and those with severe HIE. Results Among the 98 patients with HIE that received TH, 2 (2%) had mild HIE, 75 (77%) had moderate HIE and 21 (21%) had severe HIE on admission. Mortality rate was 10% (10/98) and median length of stay was 12 days [IQR 10-16]. Total mean cost per infant using the CNN algorithm was $32,727 (SD $23,751 and correlated highly to the CPSS mean $28.373(SD $28.989) (r=0.93, p<0.01). There was no significant difference in mean total costs estimated between the algorithms ($1051, 95% CI $-1073, $3174). There was a strong correlation between cost estimates using the CNN algorithm and CPSS in nursing, physician, transfusion and indirect costs (r range 0.94-0.99) (Figure 1). Mean daily costs per infant with mild/moderate HIE ($1579, SD 808) were lower compared to infants with severe HIE ($2069, SD 1518). In both groups, daily costs were higher in the first days of hospitalization and slightly decreased over time (Figure 2). Conclusion The CNN algorithm accurately predicts hospital stay costs for infants diagnosed with HIE and received TH in our centre. Severity of encephalopathy and severity of illness are associated with higher hospital costs.

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)0.002
metaresearch head score (Gemma)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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