Evidence map›Paper›PMID 35347527›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2022

Qpack-a Python package for QASAT-quantitative scale for grading cerebral blood flow, autonomic testing, and skin biopsies.

Peter Novak

Abstract read
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In one paragraph

Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

10 citing papers in PubMed, 12 citations in OpenAlex.

  1. Utilization of machine learning in diagnosis of postural tachycardia syndrome (POTS).Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2026
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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

1 author at 1 institution in 1 country.

Peter NovakAutonomic Laboratory, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, 1153 Centre Street, Boston, MA, 02103, USA. pnovak2@bwh.harvard.edu.ORCID http://orcid.org/0000-0002-5971-1208
Brigham and Women's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative grading of testing has research and clinical relevance. QASAT (quantitative scale for grading of cardiovascular reflex tests, transcranial Doppler, sudomotor testing, and small fiber densities from skin biopsies) is an objective instrument for grading dysautonomia, related small fiber neuropathy and cerebral blood flow. QASAT uses established autonomic tests (deep breathing, Valsalva maneuver, tilt test, sudomotor test) and skin biopsies for assessment of small fibers. Calculations of scores are complex. This paper presents a qpack-an open source software package that implements QASAT in a Python programming language. The qpack automatically generates reproducible scores of each test and reduces calculation errors. Datasets for verifying the correct qpack implementation are provided. The goal of qpack is to facilitate availability, reproducibility, and quality of autonomic studies and skin biopsies for assessment of small fibers. Qpack is easy to use with standard Python distributions, can be incorporated into routine clinical or research autonomic testing and it is freely available.

Indexed as

Autonomic Nervous System DiseasesSmall Fiber NeuropathyAutonomic Nervous SystemBiopsyCerebrovascular CirculationHumansReproducibility of ResultsAutonomicAutonomic testingDysautonomiaElectrochemical skin conductanceESCQASATQpackSkin biopsiesSmall fiber neuropathy

Identifiers

PMID35347527
OpenAlexW4220887470

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.