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Record W2441324087 · doi:10.1155/2016/1916387

Incidence, Characteristics, and Prognosis of Incidentally Discovered Hepatocellular Carcinoma after Liver Transplantation

2016· article· en· W2441324087 on OpenAlexaff
Walid El Moghazy, Samy Kashkoush, Glenda Meeberg, Norman Kneteman

Bibliographic record

VenueJournal of Transplantation · 2016
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHepatocellular carcinomaMedicineLiver transplantationIncidence (geometry)TransplantationAlgorithmInternal medicineMathematics

Abstract

fetched live from OpenAlex

Background. We aimed to assess incidentally discovered hepatocellular carcinoma (iHCC) over time and to compare outcome to preoperatively diagnosed hepatocellular carcinoma (pdHCC) and nontumor liver transplants. Methods. We studied adults transplanted with a follow-up of at least one year. Patients were divided into 3 groups according to diagnosis of hepatocellular carcinoma. Results. Between 1990 and 2010, 887 adults were transplanted. Among them, 121 patients (13.6%) had pdHCC and 32 patients (3.6%) had iHCC; frequency of iHCC decreased markedly over years, in parallel with significant increase in pdHCC. Between 1990 and 1995, 120 patients had liver transplants, 4 (3.3%) of them had iHCC, and only 3 (2.5%) had pdHCC, while in the last 5 years, 263 patients were transplanted, 7 (0.03%) of them had iHCC, and 66 (25.1%) had pdHCC (P < 0.001). There was no significant difference between groups regarding patient survival; 5-year survival was 74%, 75.5%, and 77.3% in iHCC, pdHCC, and non-HCC groups, respectively (P = 0.702). Patients with iHCC had no recurrences after transplant, while pdHCC patients experienced 17 recurrences (15.3%) (P = 0.016). Conclusions. iHCC has significantly decreased despite steady increase in number of transplants for hepatocellular carcinoma. Patients with iHCC had excellent outcomes with no tumor recurrence and survival comparable to pdHCC.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.448

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)0.0000.000

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 head0.225
Teacher spread0.204 · 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.

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

Citations10
Published2016
Admission routes1
Has abstractyes

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