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Record W4388048138 · doi:10.5826/dpc.1304a273

Cutaneous Malignant Melanoma in Chile: Differences in Tumor Thickness and Overall Survival Between Patients From Public and Private Health Care Centers

2023· article· en· W4388048138 on OpenAlexaff
Guisella Martínez, Francisco Bobadilla, Francisca Kinzel, Javier Fernández, Ivo Sázunic, María Magdalena Delgado, Laura Segovia, Andrea Zamudio, Nadia Vega

Bibliographic record

VenueDermatology Practical & Conceptual · 2023
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsMedicineSocioeconomic statusRetrospective cohort studyMelanomaHealth careCohortPublic healthOdds ratioOddsInternal medicineDemographyFamily medicinePathologyEnvironmental healthPopulationLogistic regressionCancer research

Abstract

fetched live from OpenAlex

INTRODUCTION: A low socioeconomic status (SES) is associated with lower survival rates in cutaneous malignant melanoma (CMM). In South America, there are few studies that analyze CMM data according to SES. OBJECTIVES: To determine the differences in microstaging and overall survival in CMM between public and private health care centers. METHODS: Retrospective cohort study. Histopathological reports with a diagnosis of CMM from two public hospitals (PuH) and one private health care center (PrH) in Santiago from 2008 to 2018 were included. Patients' death certificates were obtained to estimate overall survival. RESULTS: 1014 MMC were found. The mean age was 58.6 ± 16.8 years and 59.9% corresponded to female patients. Of these, 33.9% received treatment at PuH and 66.1% at PrH. Patients from PuH had an increased risk of having an invasive CMM and a >1 mm thickness melanoma compared to PrH (odds ratio 2.77 and 6.06, respectively). Patients with invasive CMM from the PuH were 6.29-fold more likely to die than a patient from the PrH. CONCLUSIONS: We observed a great disparity in tumor thickness between the socioeconomic status, reflecting a later detection and lower survival rate in PuH. Our results highlight a gap on which National Public Health should focus.

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)0.000
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 score0.010
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.296
Teacher spread0.258 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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