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Record W2048354162 · doi:10.1182/blood-2009-10-249201

Genomic instability after allogeneic hematopoietic cell transplantation is frequent in oral mucosa, particularly in patients with a history of chronic graft-versus-host disease, and rare in nasal mucosa

2010· article· en· W2048354162 on OpenAlexaff
Faisal Khan, Sarah Sy, Polly Louie, Alejandra Ugarte-Torres, Noureddine Berka, Gary Sinclair, Douglas A. Stewart, James A. Russell, Jan Storek

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsMedicineOral mucosaTransplantationGraft-versus-host diseaseHematopoietic stem cell transplantationGastroenterologyMucous membrane of noseInternal medicinePathology

Abstract

fetched live from OpenAlex

Genomic instability (GI) of cells may lead to their malignant transformation. Carcinoma after hematopoietic cell transplantation (HCT) frequently involves some (eg, oral) but not other (eg, nasal) epithelia. We examined GI in oral and nasal mucosal specimens from 105 subjects, including short-term (7-98 days, n = 32) and long-term (4-22 yrs, n = 25) allogeneic HCT survivors. Controls included autologous HCT survivors (n = 11), patients treated with chemotherapy without HCT (n = 9) and healthy controls (n = 27). GI was detected in 60% oral versus only 4% nasal specimens in long-term allogeneic HCT survivors (P < .001). None of the controls showed GI. In oral specimens, GI was significantly associated with history of oral chronic graft-versus-host disease (cGVHD). We conclude that GI after HCT is frequent in some (oral) but rare in other (nasal) epithelia. This may explain why some epithelia (especially those involved with cGVHD) are prone to develop cancer.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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 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

Citations39
Published2010
Admission routes1
Has abstractyes

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