Evidence map›Paper›PMID 36530340›Full record

ReviewCurrent breast cancer reports2022

Disparities in Time to Treatment for Breast Cancer: Existing Knowledge and Future Directions in the COVID-19 Era.

Genevieve A Fasano, Solange Bayard, Tamika Gillot, Zuri Hannibal, Marian Pedreira, Lisa Newman

Abstract readReview
In one paragraph

Review in Current breast cancer reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
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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

6 authors.

Genevieve A FasanoDepartment of Surgery, New York-Presbyterian-Weill Cornell Medicine, New York, NY USA.
Solange BayardDepartment of Surgery, New York-Presbyterian-Weill Cornell Medicine, New York, NY USA.
Tamika GillotDepartment of Surgery, New York-Presbyterian-Weill Cornell Medicine, New York, NY USA.
Zuri HannibalDepartment of Surgery, New York-Presbyterian-Weill Cornell Medicine, New York, NY USA.
Marian PedreiraDepartment of Surgery, New York-Presbyterian-Weill Cornell Medicine, New York, NY USA.
Lisa NewmanDepartment of Surgery, New York-Presbyterian-Weill Cornell Medicine, New York, NY USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose of Review: Despite significant advances in detection and treatment for breast cancer, the breast cancer mortality rate for Black women remains 40% higher than that for White women. Timely work-up and treatment improve outcomes, yet no gold standard exists for which to guide providers. Recent Findings: A large body of literature demonstrates disparities in time to treatment for breast cancer, and most studies show that Black women receive treatment later than their White counterparts. The COVID-19 pandemic has been projected to worsen these disparities, but the extent of this impact remains unknown. Summary: In this review, we describe the available evidence on disparities in time to treatment, potential drivers, and possible mitigation strategies. Future research must address how the COVID-19 pandemic has impacted the timely treatment of breast cancer patients, particularly populations vulnerable to disparate outcomes. Improved access to multidisciplinary breast programs, patient navigation services, and establishment of standards for timely treatment are necessary.

Indexed as

Breast cancerCancer screeningDisparitiesTime to treatment

Identifiers

PMID36530340
PMCPMC9735127

What OpenQuestion holds

Textmetadata
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.