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Record W4312116747 · doi:10.51731/cjht.2022.530

Post‒COVID-19 Condition Treatment and Management Rapid Scoping Review

2022· article· en· W4312116747 on OpenAlexaboutno aff
Yi‐Sheng Chao, Sarah C. McGill, Michelle Gates, Thyna Vu, Angie Hamson

Bibliographic record

VenueCanadian Journal of Health Technologies · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionCoronavirus disease 2019 (COVID-19)Medical diagnosisIntensive care medicineMEDLINESevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychiatryPathologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This rapid living scoping review provides an up-to-date map of the latest published evidence, and identifies research gaps regarding the treatment and management for post–COVID-19 condition. Most of the included studies were from a few countries, particularly the US, the UK, and Canada. Also, most of the studies were case reports or series, meaning they included few participants. Most of the identified published research on treatments for post‒COVID-19 condition focused on: vaccines administered 3 months after initial infection pharmacological interventions for cardiovascular, neurological, and pulmonary symptoms and diagnoses non-pharmacological interventions for pulmonary symptoms. There were fewer studies related to other types of symptoms and organ systems. There were notable evidence gaps across all treatments for post–COVID-19 condition, as noted by most of the evidence being from case reports, in which physicians may more often report patients with uncommon symptoms or diagnoses.

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)0.016
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.092
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0120.010
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0290.004

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 head0.035
GPT teacher head0.362
Teacher spread0.327 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Health TechnologiesSame topicLong-Term Effects of COVID-19French-language works237,207