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Abstract P6-09-07: The development of a prediction tool for moderate to severe diarrhea in HER-2/hormone positive metastatic breast cancer (MBC) patients receiving lapatinib in combination with letrozole (L-L)

2015· article· en· W1679330090 on OpenAlexaff
George Dranitsaris, Mario E Locouture

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsAugmentium Pharma Consulting (Canada)
Fundersnot available
KeywordsMedicineLapatinibLetrozoleDiarrheaDiscontinuationInternal medicineGeeMetastatic breast cancerReceiver operating characteristicCancerOncologyBreast cancerGeneralized estimating equationStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract Background: For patients with HER-2/hormone positive MBC, the addition of lapatinib to letrozole is associated improvements in tumour response rates and a prolongation of progression free survival. However, moderate to severe diarrhea (≥ grade 2) is a potentially serious toxicity which can lead to dose reductions, delays, hospitalizations and even the premature discontinuation of treatment. Patient care could substantially be improved if these diarrhea events could be accurately predicted through the use of validated and easy-to-use mathematical models. In this study, the development of a repeated measures model to predict the risk of ≥ grade 2 diarrhea prior to each month of L-L therapy is described. Methods Data from 111 patients who received the L-L combination as part of a clinical trial were reviewed [Johnston, 2009]. Generalized estimating equations (GEE) were used to develop the final risk model using a backwards elimination process. Internal validation of the final regression coefficients was done using nonparametric bootstrapping. A risk scoring algorithm (range 0-250) was then derived from the final model coefficients. A receiver operating characteristic curve (ROC) analysis was then undertaken to measure the predictive accuracy of the final scoring algorithm. Results: Presence of skin and lung metastases at baseline, cumulative lapatinib dose and Hg level (nadir) were identified as being important predictors for ≥ grade 2 diarrhea. There was also a negative association between time on therapy and risk of diarrhea where a higher frequency was observed in the first few months. The ROC analysis indicated good predictive accuracy with an area under the curve of 0.80 (95%CI: 0.72 – 0.88). Prior to each new month of therapy, patients with risk scores > 125 units would be considered at high risk for developing ≥ grade 2 diarrhea. Conclusions: Risk of ≥ grade 2 diarrhea is associated with cumulative lapatinib exposure, disease related factors as well as Hg level. The planned external validation and eventual clinical application of this prediction tool will be an important source of risk information for the practicing oncologist and can enhance patient care by optimizing preventative therapies earlier in a proactive manner. Citation Format: George Dranitsaris, Mario E Locouture. The development of a prediction tool for moderate to severe diarrhea in HER-2/hormone positive metastatic breast cancer (MBC) patients receiving lapatinib in combination with letrozole (L-L) [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-09-07.

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)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.093
GPT teacher head0.407
Teacher spread0.313 · 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 designSimulation or modeling
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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Citations0
Published2015
Admission routes1
Has abstractyes

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