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Record W2120183688 · doi:10.14740/jh224e

Arterial and Venous Estimation of Hemoglobin: A Correlational Study

2015· article· en· W2120183688 on OpenAlexvenueno aff
Farhan Al Enezi, Abdullah Al Anazi, Mohammed Al Mutairi, Mohammed Al Shahrani, Shoeb Qureshi, Manjush Karthika

Bibliographic record

VenueJournal of Hematology · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemoglobinEstimationInternal medicineCardiology

Abstract

fetched live from OpenAlex

Background: The objective of this study was to identify the correlation between arterial blood gas (ABG) hemoglobin and venous hemoglobin in acutely ill patients. Methods: This was a prospective observational study on ABG and venous hemoglobin in samples drawn from 103 (59 males and 44 females) patients who presented to acute care areas like emergency room and intensive care units of a quaternary care center. ABG hemoglobin was estimated from point of care testing and venous hemoglobin was obtained from the sample sent to the laboratory. The data were entered into a specifically designed database and analyzed statistically by statistical package for the social sciences (SPSS) version 20. Results: Although there were some serious pathological involvement and associated comorbidities, statistical analysis on correlation between ABG and venous hemoglobin showed a strong positive correlation between ABG and venous hemoglobin (P < 0.01). Conclusion: ABG analysis of hemoglobin could be considered as an alternate tool for hemoglobin quantification, except in rarest scenarios that require a precise estimation of hemoglobin concentration. We also conclude that rapid estimation of hemoglobin by ABG analysis would enhance diagnostic approaches and prognostic aspects of critically ill patients. J Hematol. 2015;4(3):187-192 doi: http://dx.doi.org/10.14740/jh224e

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.128
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.295
Teacher spread0.270 · 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.

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

Citations4
Published2015
Admission routes1
Has abstractyes

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