Evidence map›Paper›PMID 37291827›Full record

ArticleThe American journal of case reports2023

Cryptogenic Abdominal Pain: Recognizing False Leads.

Amna Anees

Open access · hybridAbstract readCase Reports
In one paragraph

Article in The American journal of case reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 90% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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.

Amna AneesDepartment of Internal Medicine, West Virginia University-Charleston Division, Charleston, WV, USA.
West Virginia University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Lead toxicity is a rare yet serious condition which can be difficult to diagnose due to vague presenting symptoms. Other pathologies can also mimic the symptoms of chronic lead toxicity, making an already difficult diagnosis more challenging. There are multiple environmental and occupational contributors to lead toxicity. A thorough history and an open differential is the key to diagnosing and treating this rare disease. With increasing diversity of our patient population, we should keep an open differential, as the epidemiological features of presenting concerns have diversified as well. CASE REPORT A 47-year-old woman presented with persistent nonspecific abdominal pain despite extensive prior work, surgeries and a prior diagnosis of porphyria. This patient was eventually diagnosed as having lead toxicity when her most recent work-up for abdominal pain revealed no urine porphobilinogen and a high lead level. The cause of lead toxicity was attributed to be an eye cosmetic called "Surma", which can have variable lead levels. Chelation therapy was advised for the patient. CONCLUSIONS It is important to recognize the difficulty in this challenging diagnosis for nonspecific abdominal pain and to eliminate the mimickers. This case is interesting because the patient was initially diagnosed with porphyria, highlighting how heavy metals, lead in this case, can lead to a false-positive diagnosis of porphyria. Accurate diagnosis requires awareness of the role of urine porphobilinogen, checking lead levels, and an open differential. This case also emphasizes the importance of avoiding anchor bias to make a timely diagnosis of lead toxicity.

Indexed as

LeadLead PoisoningAbdominal PainFemaleHumansMiddle AgedPorphobilinogenLeadPorphobilinogen

Identifiers

PMID37291827
PMCPMC10259268
OpenAlexW4375948653

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.