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Record W3087338540 · doi:10.1016/j.xjtc.2020.09.003

Commentary: Opening a window when surgeons face awesome responses to immunotherapy

2020· letter· en· W3087338540 on OpenAlexaff
Stephan Söder, Moïshe Liberman

Bibliographic record

VenueJTCVS Techniques · 2020
Typeletter
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineImmunotherapyImmune checkpointBasal cellOncologyInternal medicineCancer

Abstract

fetched live from OpenAlex

Immunotherapy has profoundly changed the paradigm in the treatment of several solid tumors over the past decade. In metastatic and locally advanced cutaneous squamous cell carcinomas not amenable to curative resection, immune checkpoint inhibition with cemiplimab have shown a 47% objective response rate.1 Another phase 2 open-label study including patients with locally advanced cutaneous squamous cell carcinoma, showed a 44% objective response rate with cemiplimab using the Response Evaluation Criteria in Solid Tumors version 1.1.

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)0.008
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.052
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0050.011
Open science0.0050.003
Research integrity0.0520.063
Insufficient payload (model declined to judge)0.0160.012

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 head0.032
GPT teacher head0.316
Teacher spread0.284 · 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 designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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