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Record W2769918119 · doi:10.7759/cureus.1847

Hypercalcemia of Malignancy: An Emergency Medicine Simulation

2017· article· en· W2769918119 on OpenAlexafffund
Raman Sohi, Gillian Sheppard

Bibliographic record

VenueCureus · 2017
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMemorial University of Newfoundland
FundersFaculty of Medicine, Memorial University of NewfoundlandMemorial University of Newfoundland
KeywordsMedicineMalignancyEmergency departmentIntensive care medicineCancerAdverse effectInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Hypercalcemia is a poor prognostic factor associated with malignancy. The signs and symptoms of hypercalcemia that the patients present to the emergency department are vague and often overlap with the general symptoms of cancer itself or the adverse effects of the chemotherapy. Given that the development of hypercalcemia of malignancy can present with imminent danger to the patient and is a treatable condition, emergency physicians should know how to recognize and treat it. It also marks a time at which discussions regarding plans of care should be initiated with the patients. In this report, we describe a simulation case that can be used to train emergency medicine residents to both recognize and treat hypercalcemia of malignancy and to initiate the discussion of goals of care.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0030.003
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 head0.110
GPT teacher head0.436
Teacher spread0.326 · 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 designSimulation or modeling
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
Published2017
Admission routes2
Has abstractyes

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