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Record W4286325775 · doi:10.2196/37888

From the Cochrane Library: Interventions for Cellulitis and Erysipelas

2022· article· en· W4286325775 on OpenAlexvenueno aff
Ani Oganesyan, Torunn E Sivesind, Robert P. Dellavalle

Bibliographic record

VenueJMIR Dermatology · 2022
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsErysipelasCellulitisMedicineDermatologyPsychological interventionCochrane LibraryIntensive care medicineInternal medicineMeta-analysisNursing

Abstract

fetched live from OpenAlex

Cellulitis and erysipelas are types of skin and soft tissue infections (SSTIs) that occur when bacteria, commonly group A beta-hemolytic Streptococcus and Staphylococcus, enter through breaks in the skin.Cellulitis infects the dermis and subcutaneous fat, while erysipelas is a more superficial variant, affecting the superficial dermal lymphatics and adjacent tissues.Untreated, these conditions may result in life-threatening conditions including sepsis, gangrene, or necrotizing fasciitis [1].Due to the potential risks associated with these conditions, evidence-based research to inform up-to-date treatment guidelines is critical; Table 1 provides guidelines for reference.

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)0.003
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.138
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.1380.007

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 head0.029
GPT teacher head0.337
Teacher spread0.309 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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