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Abstract P4-14-09: Platinum-based chemotherapy in early-stage triple negative breast cancer: A meta-analysis

2020· article· en· W3013899227 on OpenAlexaff
Ramy Saleh, Michelle B. Nadler, Alexandra Desnoyers, Rouhi Fazelzad, Eitan Amir

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineInternal medicineHazard ratioOncologyTriple-negative breast cancerBreast cancerTaxaneAnthracyclineChemotherapyConfidence intervalMeta-analysisCancerSurgery

Abstract

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Abstract Background: The addition of platinum agents to anthracycline and taxane-based chemotherapy in early-stage triple negative breast cancer (TNBC) patients improves pathological complete response (pCR). Long-term outcomes, such as disease-free survival (DFS) and overall survival (OS), have not been well-established. Methods: A systematic literature review identified studies using platinum-based treatment in TNBC patients in the neoadjuvant or adjuvant setting with reportable long-term outcomes. Hazard ratios (HR) from collected data were pooled in a meta-analysis using generic inverse-variance and random effects modeling. Subgroup analyses were conducted based on treatment setting (neoadjuvant vs. adjuvant) and study design (retrospective vs. randomized controlled trials). When available, odds ratios (OR) for commonly reported safety and tolerability measures such as treatment-related death, treatment discontinuation without progression, dose reduction, neuropathy, renal impairment and hematological toxicity were calculated. Results: Seven studies comprising 1699 patients met the inclusion criteria. Median follow up was 54.5 months. All studies reported DFS and 4 studies reported OS. DFS was significantly better in platinum-based treatment (HR 0.71, 95% confidence interval (CI) 0.53-0.96; P = 0.03). However, OS was no different with a higher number of events in the platinum-based treatment arm (HR 1.17, 95% CI 0.81-1.69; P = 0.42). There was no significant difference between treatment settings (p = 0.74) or between study designs (p = 0.74), although a higher HR for OS was observed for studies in the adjuvant (HR 1.34, 95% CI 0.54-3.35) compared to neoadjuvant setting (HR 1.14, 95% CI 0.76-1.70). The reporting of toxicity was suboptimal with most studies not reporting safety and tolerability metrics clearly. Platinum-based treatment was associated with more neutropenia and thrombocytopenia and treatment discontinuation, however, the magnitude of this effect could not be estimated accurately. Conclusions: Platinum-based treatment improves DFS but has no effect on OS and increases toxicity. The discordant effect of platinum-based treatment on DFS and OS suggest the potential development of platinum resistance and worse outcomes after recurrence. The higher magnitude of discordance in adjuvant compared to neoadjuvant studies suggests an effect of platinum-based therapy on loco-regional control prior to surgery rather than prevention of distant metastatic disease. Platinum-based chemotherapy cannot be recommended in unselected patients with TNBC. Citation Format: Ramy Saleh, Michelle B Nadler, Alexandra Desnoyers, Rouhi Fazelzad, Eitan Amir. Platinum-based chemotherapy in early-stage triple negative breast cancer: A meta-analysis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-14-09.

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)0.012
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.023
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0170.059
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.230
GPT teacher head0.478
Teacher spread0.248 · 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 designMeta-analysis
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
Published2020
Admission routes1
Has abstractyes

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