MétaCan
Menu
Back to cohort
Record W2976344683 · doi:10.14740/jmc3361

Necrotizing Fasciitis of the Breast: A Case Report and Literature Review

2019· article· en· W2976344683 on OpenAlexvenueno aff
Neesa Fadaee, Michelle Ong, Mohammed Mansour Alaskari

Bibliographic record

VenueJournal of Medical Cases · 2019
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFasciitisFasciaSurgeryDebridement (dental)Left breastErythemaLesionSurgical debridementBreast cancerInternal medicine

Abstract

fetched live from OpenAlex

A 51-year-old woman presented to our facility with an open wound in the left breast. This was associated with a hard, non-mobile, tender lesion palpable underneath. The wound contained central necrosis with surrounding purulent discharge and accompanying erythema. Following radical debridement of the breast down to the pectoralis fascia the patient had a vacuum-assisted closure (VAC) device dressing applied. Histological examination was consistent with necrotizing fasciitis of the breast.

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)0.001
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0030.001
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 head0.017
GPT teacher head0.328
Teacher spread0.311 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Medical CasesSame topicStreptococcal Infections and TreatmentsFrench-language works237,207