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Record W4317105116 · doi:10.18295/squmj.1.2023.001

Predictors of Hypersensitivity Reactions to Platinum-Based Chemotherapy in a Tertiary Care Hospital in Oman

2023· article· en· W4317105116 on OpenAlexaff
Bushra Salman, Fatma Al-Rasbi, Nameer M AlWard, Khalid Al-Baimani, Ikram Burney, Eman Abdullah, Buthaina Al-Azizi, Khulood Al-Mishaikhi, Ibrahim Al‐Zakwani, Mansour Al‐Moundhri

Bibliographic record

VenueSultan Qaboos University medical journal · 2023
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsMedicineOxaliplatinConcomitantCarboplatinCisplatinInternal medicineCohortRetrospective cohort studyConfidence intervalCancerMann–Whitney U testChemotherapyOncologyColorectal cancer

Abstract

fetched live from OpenAlex

Objectives: This study aimed to estimate the prevalence and evaluate risk factors of hypersensitivity reactions (HSRs) to platinum-based compounds (PBCs) in cancer patients. PBCs play an important role in cancer therapy. However, one of the drawbacks of PBCs is the occasional occurrence of HSRs, which can lead to serious consequences. Methods: This retrospective case control study was conducted from January 2013 to December 2020 at Sultan Qaboos University Hospital, Muscat, Oman and included patients who received any PBC for the management of non-haematological cancers. Data regarding demographic characteristics and diseases and treatment details were collected from the hospital's electronic database. The data were quantitatively described and Student's t-test and Wilcoxon Mann-Whitney tests were used to detect significant differences. Results: <0.001) were significant predictors of HSRs to PBCs. The majority of the reactions were of mild to moderate severity, and the rechallenge rate after HSR development was 13%. Conclusion: HSRs to PBCs impact therapy decisions and understanding the risk factors is important to improve treatment outcomes in cancer patients.

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)0.001
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 score0.149
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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