Evidence map›Paper›PMID 37296941›Full record

ReviewCancers2023

To Bev or Not to Bev during Ovarian Cancer Maintenance Therapy?

Jacek Jan Sznurkowski

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Review
  3. Hypoxia alters the response of ovarian cancer cells to the mitomycin C drug.Frontiers in cell and developmental biology · 2025
    Article
  4. Article
  5. Review
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Jacek Jan SznurkowskiProfesor Sznurkowski Podmiot Leczniczy, 81-346 Gdynia, Poland.ORCID 0000-0001-9218-0245
Gdynia Maritime University · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaintenance therapy with PARP inhibitors and bevacizumab is approved for ovarian cancer treatment in the first and second line settings, but selecting the optimal sequence is challenging due to restrictions on using the same medication twice. This review aims to establish guidelines for ovarian cancer maintenance therapy based on the strength of scientific evidence, the most effective treatment strategy, and the impact on the healthcare system.

methodsSix questions were formulated to evaluate the scientific evidence supporting different maintenance therapy options using the AGREE II guideline evaluation tool. The questions address the acceptability of reusing the same medication, the efficacy of bevacizumab and PARP inhibitors in the first and second line settings, the comparative efficacy of these medications, the potential benefit of combination maintenance therapy, and the economic impact of maintenance therapy.

resultsBased on the available evidence, bevacizumab should be preserved for second line maintenance therapy, and maintenance therapy with PARP inhibitors should be offered to all advanced ovarian cancer patients who have responded to first line platinum-based chemotherapy. Additional molecular predictors for bevacizumab efficacy are needed.

conclusionsThe presented guidelines offer an evidence-based framework for selecting the most effective maintenance therapy for ovarian cancer patients. Further research is necessary to refine these recommendations and improve outcomes for patients with this disease.

Indexed as

AGREEbevacizumabguidelinesmaintenance therapyovarian cancerPARPi

Identifiers

PMID37296941
PMCPMC10251846
OpenAlexW4378900956

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.