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Fluorosis detected by trephine biopsy

2008· article· en· W2004796866 on OpenAlexaff
Alina S. Gerrie, Suhas A. Kotecha, Alden Chesney, Anita Rachlis, Matthew C. Cheung

Bibliographic record

VenueBritish Journal of Haematology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineOsteosclerosisTrephineSkeletal fluorosisBiopsyPathologyRadiologySurgeryFluorideDental fluorosis

Abstract

fetched live from OpenAlex

A 45-year-old man presented with low back pain and difficulty ambulating. A magnetic resonance imaging scan of the brain and lumbosacral spine demonstrated homogeneous replacement of normal marrow and routine chest radiograph revealed diffuse osteosclerosis (top left). He was referred to rule out an infiltrative marrow process. Iliac crest biopsy demonstrated normal trilineage haematopoeisis with markedly sclerotic cortical bone and thickened bony trabeculae (top right). There was no evidence of metastatic cancer or granulomatous disease. Bone mineral density Z-scores of lumbar spine and femoral neck were +10·8 and +10·9 respectively, confirming osteosclerosis. On further history, the patient admitted to ingesting five tubes of toothpaste per week for 30 years. Serum fluoride level was 44·2 μmol/l (reference 1·0–4·6 μmol/l), supporting a diagnosis of skeletal fluorosis. Skeletal fluorosis is caused by excessive fluoride ingestion. Although under-recognized in developed nations, it affects millions worldwide due to contaminated well water. Moderate exposure leads to dental effects such as staining and pitting of teeth and damage to enamel (bottom). Long-term ingestion may lead to skeletal abnormalities including debilitating arthralgias and impairment of cervical and lumbar mobility. Symptoms have been reported to resolve slowly after removal of fluoride.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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.

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

Citations1
Published2008
Admission routes1
Has abstractyes

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