MétaCan
Menu
Back to cohort
Record W2900938763 · doi:10.14740/wjon1143w

Surgical Treatment of Trichilemmal Carcinoma

2018· article· en· W2900938763 on OpenAlexvenueno aff
De Bin Xu, Tao Wang, Zhen Liao

Bibliographic record

VenueWorld Journal of Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer and Skin Lesions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMetastasisSurgical marginMultivariate analysisLymphRetrospective cohort studyDistant metastasisSurgeryOncologyCancerInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: To investigate the clinicopathologic characteristics and prognostic factors of trichilemmal carcinoma (TC), and to determine an optimal treatment strategy for these patients. METHODS: This retrospective study enrolled consecutive patients who were admitted to the Sun Yat-sen University Cancer Center between 1998 and 2012. RESULTS: The key prognostic factors influencing the survival were lymph nodes metastasis and surgery margin. Multivariate analysis revealed that there was no risk factor for patient survival. CONCLUSIONS: Surgery margin and lymph nodes metastasis were prognostic factors that influenced the treatment outcome. Simple excision with 1 cm margins is safe, inexpensive and effective for the treatment of TC; and postoperative follow-up of the patient to facilitate early diagnosis of the recurrence and distant metastasis is necessary. Systemic chemotherapy should be considered for the distance metastases patients.

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: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.042
GPT teacher head0.369
Teacher spread0.327 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of OncologySame topicCancer and Skin LesionsFrench-language works237,207