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Record W4415209207 · doi:10.14740/jocmr6339

Effectiveness of Antibiotic Regimens in Reducing White Blood Cell Count Within Three to Five Days in Febrile Leukocytosis Treated With Ambulatory Therapy

2025· article· en· W4415209207 on OpenAlexvenueno aff
Woraphat Tumporn, Thanin Lokeskrawee, Natthaphon Pruksathorn, Suppachai Lawanaskol, Jayanton Patumanond, Suwapim Chanlaor, Wanwisa Bumrungpagdee, Chawalit Lakdee

Bibliographic record

VenueJournal of Clinical Medicine Research · 2025
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsnot available
Fundersnot available
KeywordsLeukocytosisRegimenWhite blood cellAmbulatoryAntibioticsMulticenter study

Abstract

fetched live from OpenAlex

Background: In the absence of sepsis, patients with fever and leukocytosis in the emergency department (ED) are often treated with ambulatory parenteral antibiotics at the physician's discretion. Identifying effective regimens for reducing white blood cell (WBC) count and improving clinical outcomes may support standardized ED care. Methods: This retrospective cohort included adult ED patients with fever and leukocytosis, but without sepsis, in whom basic investigation revealed no clear source of infection. Patients received one of three regimens: (A) single-day intravenous (IV) ceftriaxone followed by oral cefixime, (B) 3-day IV ceftriaxone followed by oral cefixime, or (C) 3-day IV ceftriaxone plus oral doxycycline from day 1. Demographics, baseline data, and laboratory results were collected. Follow-up assessments included WBC count and clinical improvement. Treatment probabilities were estimated with multinomial logistic regression, and stabilized inverse probability of treatment weighting (IPTW) were applied in weighted quantile regression. Results: (95% CI: -7.0 to -6.2) for regimen A. Differences were not significant (Wald test, P = 0.484), but graphical analysis suggested the steepest decline with regimen C. Conclusion: Though regimen C showed the steepest WBC decline and fewer failures, the study was markedly underpowered (< 10%). Larger multicenter studies are required to confirm these findings.

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)0.011
metaresearch head score (Gemma)0.002
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 score0.043
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.096
GPT teacher head0.468
Teacher spread0.371 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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