MétaCan
Menu
← Back to cohort
Record W3081653085 · doi:10.7759/cureus.10138

Managing a Locally Advanced Cervix Cancer Patient With COVID-19: Lessons Learned

2020· article· en· W3081653085 on OpenAlexaff
David D’Souza, Vikram Velker, Lucas C. Mendez, Pencilla Lang, Akira Sugimoto

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineCervixCoronavirus disease 2019 (COVID-19)CancerSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCervical cancerIntensive care medicineDiseaseMedical physicsGeneral surgeryInternal medicinePathologyInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

We present the case of a woman diagnosed with coronavirus disease 2019 (COVID-19) while undergoing chemoradiation for locally advanced cervix cancer. This diagnosis had implications for the treatment of her cancer, and a number of important decisions had to be made. We present the issues that arose and how her oncologic care was managed.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0040.006
Open science0.0020.004
Research integrity0.0070.014
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 head0.145
GPT teacher head0.432
Teacher spread0.287 · 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 designCase report
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCureus→Same topicCOVID-19 and healthcare impacts→French-language works237,207→