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Record W3158109924 · doi:10.2519/jospt.2021.0106

Humility and Acceptance: Working Within Our Limits With Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

2021· article· en· W3158109924 on OpenAlexaff
Simon Décary, Isabelle Gaboury, Sabrina Poirier, Christiane Ribas Garcia, Scott A. Simpson, Michelle Bull, Darren A. Brown, Frédérique Daigle

Bibliographic record

VenueJournal of Orthopaedic and Sports Physical Therapy · 2021
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)PandemicRehabilitationChronic fatigue syndromeEncephalomyelitisQuality of life (healthcare)Physical therapyDiseaseAdverse effectIntensive care medicinePsychiatryNursingInfectious disease (medical specialty)Internal medicineMultiple sclerosis

Abstract

fetched live from OpenAlex

Synopsis The term long COVID was coined by patients to describe the long-term consequences of COVID-19. One year into the pandemic, it was clear that all patients—those hospitalized with COVID-19 and those who lived with the disease in the community—were at risk of developing debilitating sequelae that would impact their quality of life. Patients with long COVID asked for rehabilitation. Many of them, including previously healthy and fit clinicians, tried to fight postviral fatigue with exercise-based rehabilitation. We observed a growing number of patients with long COVID who experienced adverse effects from exercise therapy and symptoms strikingly similar to those of myalgic encephalomyelitis (ME). Community-based physical therapists, including those in private practice, unaware of safety issues, are preparing to help an influx of patients with long COVID. In this editorial, we expose growing concerns about long COVID and ME. We issue safety recommendations for rehabilitation and share resources to improve care for those with postviral illnesses. J Orthop Sports Phys Ther 2021;51(5):197–200. doi:10.2519/jospt.2021.0106

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.023
GPT teacher head0.305
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations85
Published2021
Admission routes1
Has abstractyes

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