ArticleCureus2023
Evaluation of Retinal Nerve Fiber Layer and Macular Thickness Pre- and Post-Chemotherapy With Carboplatin and Paclitaxel in Patients With Endometrial and Ovarian Cancer.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Carboplatin and paclitaxel are two standard chemotherapeutic agents known to cause neurotoxicity. In this study, we aim to evaluate the toxicity of these agents by measuring the peripapillary retinal nerve fiber layer (RNFL) and macular thickness in patients with endometrial and ovarian cancers who are receiving them. Methods A one-year prospective cohort study involving 28 patients who were treated intravenously with carboplatin (200-400 mg/m
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Registered trials
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.