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Record W4379260260 · doi:10.1002/jgf2.630

Hypomagnesemia because of nedaplatin for cervical cancer: A case report

2023· article· en· W4379260260 on OpenAlexaff
Jun Komoda, Tetsuya Hori, Kotaro Sato, K Kusajima, Taro Shimizu

Bibliographic record

VenueJournal of General and Family Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsCentre for Family Medicine
Fundersnot available
KeywordsNedaplatinHypomagnesemiaMedicineCancerCervical cancerInternal medicineSurgeryAnesthesiaMagnesiumChemotherapyCisplatin

Abstract

fetched live from OpenAlex

A 76-year-old woman with cervical cancer was treated with nedaplatin, a platinum-based drug. After the initiation of the treatment, she became aware of numbness, dizziness, and loss of appetite. Exploration of the causes revealed no clues, but blood tests revealed hypocalcemia and hypomagnesemia. She was treated with intravenous calcium and magnesium, which resolved calcium, magnesium levels, and her symptoms. She was diagnosed with hypomagnesemia because of nedaplatin. Regular follow-up is necessary for patients during or after nedaplatin. Awareness of electrolyte disturbances may elucidate the accurate diagnosis even in patients with obscure symptoms, particular in undergoing or following anticancer therapies.

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)0.000
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0050.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.058
GPT teacher head0.350
Teacher spread0.292 · 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
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

Citations2
Published2023
Admission routes1
Has abstractyes

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