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Record W4293480692 · doi:10.1002/oa.3156

Revisiting the differential diagnosis of the lucent bone lesion: A case of fibrous dysplasia discovered in a Polish medieval osteological collection

2022· article· en· W4293480692 on OpenAlexaff
Syed M. A. Haydar, Adnan Sheikh, Hugue A. Ouellette, Paul I. Mallinson, Peter L. Munk

Bibliographic record

VenueInternational Journal of Osteoarchaeology · 2022
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFibrous dysplasiaDifferential diagnosisMedicineRadiographyMagnetic resonance imagingRadiologyLesionEpiphysisRadiological weaponChondroblastomaAnatomyPathology

Abstract

fetched live from OpenAlex

Abstract In the short report published by Siek and Spinek, the femoral metaphyseal lesion in question was described as potentially the first described case of a simple bone cyst (SBC) from a Polish background. However, on review of the single radiograph available, we feel the primary and differential diagnoses offered are misleading from a radiographic perspective, with the imaging features overall favoring fibrous dysplasia—probable cystic subtype. As magnetic resonance imaging (MRI) or a histological diagnosis would not be possible in skeletonized remains, the diagnosis must rest on opinion‐based radiology. In this commentary, we illustrate why fibrous dysplasia is the most likely radiological diagnosis and describe why the differentials offered in the original article should not be entertained.

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)0.005
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0060.003
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 head0.021
GPT teacher head0.301
Teacher spread0.280 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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