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Record W4200194451 · doi:10.1111/joim.13427

COVID‐19 hospitalization is associated with pulmonary/diffusion abnormalities but not post‐acute sequelae of COVID‐19 severity

2021· letter· en· W4200194451 on OpenAlexaff
Grace Y. Lam, A. Dean Befus, Ronald W. Damant, Giovanni Ferrara, Desi P. Fuhr, Cheryl R. Laratta, Angela Lau, Michael K. Stickland, Rhea Varughese, Eric Wong, Maeve P. Smith

Bibliographic record

VenueJournal of Internal Medicine · 2021
Typeletter
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsAlberta HealthUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsMedicineSequelaCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakSeverity of illnessRisk factorIntensive care medicineDiseaseInternal medicinePediatricsSurgeryOutbreakVirologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Coronavirus disease-19 (COVID-19) has resulted in much acute morbidity and mortality worldwide. There is now a growing recognition of the post-acute sequela of COVID-19, termed long COVID. However, the risk factors contributing to this condition remain unclear. Here, we address the growing controversy in the literature of whether hospitalization is a risk factor for long COVID. We found that hospitalization is associated with worse pulmonary restriction and reduction in diffusion capacity at 3 months post-infection. However, the impact on mental health, functional and quality of life is equally severe in those who have and have not been hospitalized during the acute infection. These findings suggest that hospitalization is a risk factor for pulmonary complications of long COVID but not the overall severity of long COVID.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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

Citations6
Published2021
Admission routes1
Has abstractyes

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