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Considerations for Head and Neck Oncology Practices During the Coronavirus Disease 2019 (COVID-19) Pandemic: The Wuhan and Toronto Experience

2020· dataset· en· W3082719353 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAuthorea · 2020
Typedataset
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsPandemicMedicineCoronavirus disease 2019 (COVID-19)OperationalizationHead and neck cancerHead and neckHealth careDiseaseIntensive care medicineMedical emergencyCancerInternal medicineInfectious disease (medical specialty)SurgeryPolitical science

Abstract

fetched live from OpenAlex

The practices of head and neck surgical oncologists must evolve to meet the unprecedented needs placed on our healthcare system by the Coronavirus Disease 2019 (COVID-19) pandemic. Guidelines are emerging to help guide the provision of head and neck cancer care, though in practice, it can be challenging to operationalize such recommendations. Head and neck surgeons at Wuhan University faced significant challenges in providing care for their patients. Similar challenges were faced by the University of Toronto during the severe acute respiratory syndrome (SARS) pandemic in 2003. Herein, we outline our combined experience and key practical considerations for maintaining an oncology service in the midst of a pandemic.

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.

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)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.286
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.327
GPT teacher head0.530
Teacher spread0.203 · 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