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Dynamic analysis of serum Ca-125 levels during neoadjuvant chemotherapy in patients with advanced epithelial ovarian cancer: a retrospective study

2020· article· en· W3113056049 on OpenAlexaff
Adarsh Dharmarajan, A. Remya, Aswathi Krishnan

Bibliographic record

VenueInternational Journal of Reproduction Contraception Obstetrics and Gynecology · 2020
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsASTER
Fundersnot available
KeywordsMedicineChemotherapyInternal medicineOvarian cancerProportional hazards modelOncologyUnivariate analysisPathologicalCancerMultivariate analysis

Abstract

fetched live from OpenAlex

Background: Assessment of CA-125 kinetics was commonly used as a tool for tumor response to chemotherapy in ovarian cancer patients. The study aimed to determine any logarithmic/linear relationship between pre-chemotherapy and pre-operative CA-125 levels in ovarian cancer.Methods: Total 52 patients who underwent neoadjuvant chemotherapy (NACT) followed by interval cytoreductive surgery were included. CA-125 levels before starting chemotherapy, during chemotherapy and the preoperative value, with the date of measurement recorded. Cox’s proportional hazards regression was used to evaluate univariate and independent multivariable association with the effect of clinical, pathological and CA-125 kinetic parameters on outcome endpoints. Results: The study couldn’t establish any relationship in logarithmic fall of CA-125 values among ovarian cancers as a result of neo-adjuvant chemotherapy. The disease-free survival among the patients was 12.2 months.Conclusions: There is an inverse relationship between serum CA-125 levels and survival in ovarian cancer. NACT resulted in adequate fall of CA-125 levels in most of the patients, but the rate of fall was not predictive of prognosis.

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)0.001
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 score0.086
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.009
GPT teacher head0.276
Teacher spread0.266 · 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
Published2020
Admission routes1
Has abstractyes

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