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Record W3021493583 · doi:10.1101/2020.05.03.20088526

Objective Olfactory Evaluation of Self-reported Loss of Smell in a Case Series of 86 COVID-19 Patients

2020· preprint· en· W3021493583 on OpenAlexaff
Jérôme R. Lechien, Pierre Cabaraux, Carlos M. Chiesa‐Estomba, Mohamad Khalifé, Stéphane Hans, Christian Calvo‐Henríquez, Delphine Martiny, Fabrice Journé, Leigh J. Sowerby, Sven Saussez

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)AnosmiaEpidemiologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineDisease

Abstract

fetched live from OpenAlex

Abstract Objective To investigate olfactory dysfunction (OD) in patients with mild COVID-19 through patient-reported outcome questionnaires and objective psychophysical testing. Methods COVID-19 patients with self-reported sudden-onset OD were recruited. Epidemiological and clinical data were collected. Nasal complaints were evaluated with the sino-nasal outcome-22 (SNOT-22). Subjective olfactory and gustatory status was evaluated with the National Health and Nutrition Examination Survey (NHNES). Objective OD was evaluated using psychophysical tests. Results Eighty-six patients completed the study. The most common symptoms were fatigue (72.9%), headache (60.0%), nasal obstruction (58.6%) and postnasal drip (48.6%). Total loss of smell was self-reported by 61.4% of patients. Objective olfactory testings identified 41 anosmic (47.7%), 12 hyposmic (14.0%), and 33 normosmic (38.3%) patients. There was no correlation between the objective test results and subjective reports of nasal obstruction or postnasal drip. Conclusion A significant proportion of COVID-19 patients reporting OD do not have OD on objective testing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.289
GPT teacher head0.341
Teacher spread0.052 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2020
Admission routes1
Has abstractyes

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