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Clinical factors associated with malignancy and HIV status in patients with ocular surface squamous neoplasia at Kilimanjaro Christian Medical Centre, Tanzania

2011· article· en· W2323259231 on OpenAlexaff
Irma Illyes Makupa, Britta Swai, William Makupa, Valerie A. White, Susan Lewallen

Bibliographic record

VenueBritish Journal of Ophthalmology · 2011
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsVancouver General Hospital
Fundersnot available
KeywordsMedicineMalignancyTanzaniaHuman immunodeficiency virus (HIV)ReferralDermatologyInternal medicinePediatricsImmunologyFamily medicine

Abstract

fetched live from OpenAlex

AIMS: To describe the clinical characteristics of ocular surface squamous neoplasia (OSSN) in a sub-Saharan referral hospital setting according to histopathological diagnosis and HIV status. METHODS: All patients were enrolled who presented consecutively to the Kilimanjaro Christian Medical College eye department with lesions suspected to be OSSN from September 2005 to May 2007 and from February 2008 to September 2008. Clinical characteristics were documented on a standardised form, excision biopsies were performed and histopathological diagnosis was obtained on all cases. Data were analysed to look for associations among various factors. RESULTS: 150 patients were enrolled. Histopathological study showed OSSN in 88% of cases. Of these, 128 (85.6%) were under the age of 50 years and 60% were HIV positive. The median CD4 cell count was 71 cells/μl among HIV-positive cases. Independent of size, the lesions of patients who were HIV positive were more likely to be higher grade malignancy than those who were HIV negative. CONCLUSION: In a sub-Saharan setting, OSSN occurs in persons who are younger than in industrialised countries and is often associated with HIV positivity. CD4 cell counts indicate that a majority of HIV-positive patients with OSSN are significantly immunosuppressed at presentation. Higher grade malignancy in this group could indicate a more aggressive course.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.069
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.272
Teacher spread0.251 · 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 teacher head, 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

Citations39
Published2011
Admission routes1
Has abstractyes

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