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Record W4408509022 · doi:10.1188/25.cjon.e47-e51

Updating the Carboplatin Hypersensitivity Protocol: Two Case Studies

2025· article· en· W4408509022 on OpenAlexaff
Kimberly Ann Stout

Bibliographic record

VenueClinical journal of oncology nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsMiller Group (Canada)Kimberly-Clark (Canada)
Fundersnot available
KeywordsMedicineCarboplatinProtocol (science)Hypersensitivity reactionInternal medicineChemotherapyPathologyCisplatinAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Carboplatin is used to treat gynecologic cancers. Reexposure to carboplatin in the recurrent disease setting increases the risk of hypersensitivity reactions, which can be mild or cause death. A carboplatin desensitization protocol was developed to help with patient tolerance, but no standard protocol existed. OBJECTIVES: This protocol was updated to replace an older desensitization protocol, align with best practices, and bring cost savings to the institution through reduced labor and shortened hospital stays. METHODS: The updated four-bag, four-hour protocol was implemented in patient care on March 8, 2024. A total of five desensitizations were completed. Each desensitization was retrospectively reviewed and assessed for safety and tolerability using the Common Terminology Criteria for Adverse Events. FINDINGS: Five desensitizations successfully occurred for two patients between March 8 and July 13, 2024. Each desensitization saved $108 in pharmacy supply costs and $92 in pharmacy labor costs. Saved nursing costs totaled about $400 for each desensitization.

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)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.172
GPT teacher head0.587
Teacher spread0.415 · 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 designCase report
Domainnot available
GenreMethods

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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