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Record W4246950274 · doi:10.1177/229255031101900205

Giant Cell Reparative Granuloma of the Proximal Phalanx: A Case Report and Literature Review

2011· article· en· W4246950274 on OpenAlexaffvenue
A Perkins, Ali Izadpanah, Hani Sinno, C. Bernard, H. Bruce Williams

Bibliographic record

VenueCanadian Journal of Plastic Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineGiant cellCurettagePhalanxOsteoclastGranulomaCentral giant-cell granulomaCalcitoninPathologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

The present article is a case report of a 16-year-old boy who presented with a benign bony tumour, which on histological analysis suggested giant cell reparative granuloma (GCRG), but was not corroborate by blood tests. The implications of this type of tumour and the correct diagnostic requirements were investigated. The correct identification of GCRG from other giant cell-containing tumours is important because the treatment modalities for these tumours significantly differ from one another. In most cases, histological findings are sufficient to identify the tumours. In most GCRG cases, curettage is usually a curative treatment option. However, due to high recurrence rates of GCRGs, close follow-up of these patients is warranted. Also, due to osteoclastic activity of the giant cells in GCRGs, the use of drugs such as calcitonin or bisphosphonates, which inhibit osteoclast differentiation and activation, may have an important influence on future treatments or in reducing the recurrence rate of these tumours.

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)0.002
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0050.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.026
GPT teacher head0.238
Teacher spread0.211 · 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

Citations3
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Plastic SurgerySame topicBone Tumor Diagnosis and TreatmentsFrench-language works237,207