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Record W2514097089 · doi:10.1097/pec.0000000000000830

Outcome of Benign Acute Childhood Myositis

2016· article· en· W2514097089 on OpenAlexaff
Tom Rosenberg, Shmuel Heitner, Dennis Scolnik, Einav Levin Ben-Adiva, Ayelet Rimon, Miguel Glatstein

Bibliographic record

VenuePediatric Emergency Care · 2016
Typearticle
Languageen
FieldMedicine
TopicMuscle and Compartmental Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineMyositisOutcome (game theory)Intensive care medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aims of the study were to determine the evolution of benign acute childhood myositis in children and to assess the relationship between creatine phosphokinase (CPK) values and myoglobinuria. STUDY DESIGN: A retrospective study of patients with benign acute childhood myositis seen in 2 tertiary care university-affiliated pediatric hospitals during overlapping 4-year periods. METHODS: Demographic data, historical details, clinical, and laboratory results were extracted from the charts of children younger than 16 years with a CPK greater than 3 times normal. Complications, treatments, and outcomes were recorded. RESULTS: Fifty-four children were included, 43 (80%) were male, and mean age was 7.3 years (median [range], 6 [3-16] years), none showed abnormal neurological findings, manifested hematuria, or developed renal failure. Mean CPK level at presentation was 1872 IU/L (range, 511-8086 IU/L). None developed renal failure, and there were no adverse outcomes on follow-up. CONCLUSIONS: Acute childhood myositis is a predominantly benign disease. Neurological examination is usually normal and rhabdomyolysis is rare. Although severe pathological comorbid conditions must be excluded, a complete history and examination, coupled with simple blood and urine tests, can help minimize unnecessary diagnostic investigations.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.281
Teacher spread0.270 · 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 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

Citations22
Published2016
Admission routes1
Has abstractyes

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