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Human Chorionic Gonadotropin (hCG) Testing in Specimens of Tumor and Myometrial Tissues During Surgical Treatment of Gestational Trophoblastic Tumors

2015· article· en· W1455256040 on OpenAlexvenueno aff
Reda Hemida, Mohammad Arafa, Doaa Sharaf-Eldin

Bibliographic record

VenueJournal of cancer research updates · 2015
Typearticle
Languageen
FieldMedicine
TopicGestational Trophoblastic Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman chorionic gonadotropinTrophoblastic TumorMedicineGynecologyAndrologyGestationPathologyUrologyPregnancyHormoneInternal medicineBiology

Abstract

fetched live from OpenAlex

Background: Gestational trophoblastic tumors originate from trophoblastic tissues and secrete human chorionic gonadotrophin (hCG). Surgical treatment may be a line of treatment of chemoresistant cases. Objective: To evaluate the accuracy of hCG dipsticks in detection of hCG in tissues of trophoblastic tumors and healthy myometrium during surgery of trophoblastic tumors. Methods: We included 19 samples of tumor and apparently healthy myometrial tissues during surgical treatment of 5 cases of gestational trophoblastic tumors. The hCG dipstick was immersed in a solution containing 1x1 cm of tumor or myometrial tissues. The results of the tests were compared to the histopathological results. Results: The mean age of patients were 38.8 years, the mean parity was 3.4. The mean serum B-hCG level was 101,745.6 mu/ml. Except for one specimen in case 5, all results of the hCG dipsticks were concordant with final histopathologic analysis of the specimens. Sensitivity of hCG test was 100% and specificity was 90%. Conclusion: Intraoperative detection of hCG in different tissues and suspicious masses can be considered as simple, rapid, inexpensive, and reliable test. It can be used to detect the trophoblastic nature of tissues if frozen section is not available as some low resource setting countries. We recommend further larger prospective studies to compare the accuracy and reliability of this novel technique and frozen section analysis.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.148
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.126
GPT teacher head0.434
Teacher spread0.308 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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