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Record W2074904451 · doi:10.1089/apc.2007.0019

Otosyphilis in HIV-Coinfected Individuals: A Case Series from Toronto, Canada

2008· article· en· W2074904451 on OpenAlexaffabout
Sharmistha Mishra, Sharon Walmsley, Mona Loutfy, Rupert Kaul, Kenneth John Logue, Wayne L. Gold

Bibliographic record

VenueAIDS Patient Care and STDs · 2008
Typearticle
Languageen
FieldMedicine
TopicSyphilis Diagnosis and Treatment
Canadian institutionsMaple Leaf Medical ClinicUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineTinnitusSyphilisLatent SyphilisNeurosyphilisHearing lossHuman immunodeficiency virus (HIV)PediatricsPsychiatryAudiologyFamily medicine

Abstract

fetched live from OpenAlex

We sought to identify and review the clinical features and treatment outcomes of eight recent cases of otosyphilis in HIV-positive patients seen in Toronto. All patients reported tinnitus, and seven (87.5%) reported subjective hearing loss. Not taking auditory findings into consideration, four patients would be classified as having secondary syphilis, three patients as having early latent syphilis, and one patient as having latent syphilis of unknown duration. The median CD4 cell count was 370 x 10(6)/L. All patients were treated with intravenous aqueous penicillin G with regimens recommended for the treatment of neurosyphilis; four patients received adjunctive steroids. All eight patients experienced improvement in tinnitus and four of the seven (57.1%) patients with symptomatic hearing loss also experienced improvement. Otosyphilis can occur in HIV-positive individuals despite high CD4 cell counts, and is potentially reversible. Increased awareness of uncommon manifestations of syphilis in high-risk individuals is warranted to prompt appropriate investigation and treatment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.885

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations30
Published2008
Admission routes2
Has abstractyes

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