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Thymic carcinoma: A cohort study of prognostic factors after surgical resection from the European Society of Thoracic Surgeons database.

2013· article· en· W2603814443 on OpenAlexaff
Enrico Ruffini, Frank C. Detterbeck, Dirk Van Raemdonck, Gaetano Rocco, P. Thomas, Walter Weder, Alessandro Brunelli, Francesco Guerrera, Shaf Keshavjee, Nasser K. Altorki, J Schützner, Alper Toker, Lorenzo Spaggiari, Alex Arame, Eric Kian Saik Lim, Federico Venuta

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineCumulative incidenceMultivariate analysisIncidence (geometry)Thymic carcinomaRadiation therapyUnivariate analysisStage (stratigraphy)T-stageProportional hazards modelInternal medicineCohortSurgeryChemotherapyOncologyDatabaseCancer

Abstract

fetched live from OpenAlex

7602 Background: Thymic carcinomas are rare tumors which have recently been separated from thymomas due to their different histologic/clinical characteristics. Most of the current literature is composed of small series spanned over extended time periods Methods: The European Society of Thoracic Surgeons (ESTS) developed a retrospective database collecting data on patients with thymic tumors submitted to surgery (1990-2011). Out of 2,265 incident cases, there were 229 thymic carcinomas. Clinical-pathologic characteristics were analyzed including age, gender, stage (Masaoka), histologic subtypes (squamous cell/others), type of resection (complete/incomplete), tumor size, induction and adjuvant therapy (chemotherapy-ChT/radiotherapy-RT), recurrence. Primary outcome was overall survival (OS); secondary outcomes were disease-free survival (DFS) and the cumulative incidence of recurrence. Survival analysis was performed using univariate and multivariate (Cox-shared frailty) competing-risk models. Missing data were analysed using multiple-imputation techniques Results: A multidisciplinary approach (surgery, ChT and RT) was used in most patients. Induction therapy was employed in 78 patients (ChT, 53; ChT/RT 23; RT, 2). Adjuvant therapy was employed in 150 patients (ChT, 19; ChT/RT, 72; RT, 59). Complete resection (R0) was achieved in 71% of the patients. Five and 10-year OS were 60% and 35%. Five and 10-year DFS were 62% and 43%. Cumulative incidence of recurrence was 0.25, 0.32 and 0.40 at 3, 5, and 10 years. Independent OS predictors (multivariate analysis) were young age (p=0.006), stage I/II (vs. III/IV, p=0.02), R0 resection (p<0.001), adjuvant therapy (ChT, RT or both) (p=0.02). Independent predictor of recurrence (univariate analysis) was tumor size (p=0.05). Conclusions: In thymic carcinomas submitted to surgical resection, increased age,Masaoka stages III-IV and incomplete resection had a significant impact in worsening survival. Larger tumors had an increased risk of recurrence. The administration of adjuvant ChT or RT was associated with improved overall survival. A multidisciplinary approach to these rare tumors remains essential.

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)0.003
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.104
GPT teacher head0.429
Teacher spread0.325 · 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".

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Citations1
Published2013
Admission routes1
Has abstractyes

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