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Record W2981494960 · doi:10.1186/s40463-019-0369-0

A case report of severe nasal ischemia from cold agglutinin disease and a novel treatment protocol including HBOT

2019· review· en· W2981494960 on OpenAlexaff
Scott Kohlert, Laurie McLean, Dimitrios Scarvelis, Calvin Thompson

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsCold agglutinin diseaseMedicineIschemiaAnesthesiaImmunologyInternal medicineAntibody

Abstract

fetched live from OpenAlex

Cold agglutinin disease (CAD) is a rare condition leading to blood agglutination and autoimmune hemolytic anemia. Cutaneous ischemia resulting from CAD in the head and neck is uncommon. Treatment regimens and outcomes vary widely in the literature and no clear protocol exists. This manuscript describes a patient with CAD who developed severe ischemia of the nose that resolved completely without sequellae following a medical regimen of aspirin, low molecular weight heparin, nitroglycerin ointment and hyperbaric oxygen therapy (HBOT). To our knowledge, this is the first reported case where nitroglycerin ointment or HBOT was successfully employed in the treatment of this complication.

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)0.002
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.089
GPT teacher head0.355
Teacher spread0.266 · 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
Published2019
Admission routes1
Has abstractyes

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