Observational studyMedicine2017
Final diagnoses and probability of new reason-for-encounter at an urban clinic in Japan: A 4-year observational study.
Observational study in Medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed, 4 citations in OpenAlex.
- How symptoms of simple acute infections affect the SSS-8 and SSD-12 as screening instruments for somatic symptom disorder in the primary care setting.Frontiers in psychiatry · 2023Article
- Oxygen use and survival in patients with advanced cancer and low oxygen saturation in home care: a preliminary retrospective cohort study.BMC palliative care · 2020Article
- Characteristics, Preferences and Health Care Utilization in Patients Using a Dietary Supplement for Improving Sleeping Disturbances: Results from an Explorative Online Survey.Patient preference and adherence · 2020Article
- Abdominal pain and nausea in the diagnosis of streptococcal pharyngitis in boys.International journal of general medicine · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Past clinical data are not currently used to calculate pretest probabilities, as they have not been put into a database in clinical settings. This observational study was designed to determine the initial reasons for utilizing home visits or visits to an outpatient urban clinic in Japan.All family medical clinic outpatients and patients visited by the clinic (total = 11,688) over 1460 days were enrolled.We used a Bayes theorem-based clinical decision support system to analyze codes for initial reason-for-encounter (examination and final diagnosis: pretest probability) and final diagnosis of patients with fever (conditional pretest probability).Total number of reasons-for-encounter: 96,653 (an average of 1.2 reasons per visit). Final diagnosis: 62,273 cases (an average of 0.75 cases per visit). The most common reasons for initial examination were immunizations, physical examinations, and upper respiratory conditions. Regarding the final diagnosis, the combination of physical examinations and acute upper respiratory infections comprised 73.4% of cases. In cases where fever developed, the bulk of the final diagnoses were infectious diseases such as influenza, strep throat, and gastroenteritis of presumed infectious origin. For the elderly, fever often occurred with other health issues such as pneumonia, dementia, constipation, and sleep disturbances, though the cause of the fever remained undetermined in 40% of the cases.The pretest probability changed significantly based on the reason or the combination of reasons for which patients requested a medical examination. Using accumulated data from past diagnoses to modify subsequent subjective diagnoses, individual diagnoses can be improved.
Indexed as
Identifiers
What OpenQuestion holds
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.