MétaCan
Menu
Back to cohort
Record W4393922716 · doi:10.5281/zenodo.6836909

HCFMRP Sacroiliitis (v1)

2022· dataset· en· W4393922716 on OpenAlexaboutno aff
Vítor Faeda Dalto, Matheus Calil Faleiros, Marcello Henrique Nogueira‐Barbosa, Paulo Mazzoncini de Azevedo‐Marques, Natália Santana Chiari Correia, Saulo da Silva Cordeiro, Rodolfo Dias Chiari Correia, Lucas Lins de Lima, Hugo Cesar Peloggia Rodrigues

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldMedicine
TopicOsteomyelitis and Bone Disorders Research
Canadian institutionsnot available
Fundersnot available
KeywordsSacroiliitisComputer scienceGeologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Dataset composed of magnetic resonance imaging (MRI) of the sacroiliac joints of 48 patients from the Ribeirão Preto Medical School of University of São Paulo (Brazil). The selected patients are represented in 51 studies, 99 series and 594 images in the DICOM protocol, all anonymized. For each DICOM series, 6 consecutive images (slices) of the sacroiliac joints were obtained in the coronal plane, which were evaluated according to the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI diagnostic criteria. Each DICOM image is accompanied by a PSD file containing the original image and a segmentation mask in PNG format. The masks indicate the location of the sacroiliac joints. The dataset contains, therefore, 1782 image files. The files are organized into two main directories, STIR and SPAIR, representing the two fat-saturated MRI techniques we adopted for this study. Each of them is subdivided into "Positive" and "Negative", indicating whether there are findings compatible with sacroiliitis. For more information, contact us: https://mainlab.fmrp.usp.br/

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.634
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.6520.018

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 head0.036
GPT teacher head0.280
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicOsteomyelitis and Bone Disorders ResearchFrench-language works237,207