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Record W4386386493 · doi:10.1002/cncr.34999

Time to quit using <i>quit time</i> as a lung cancer screening eligibility criterion

2023· editorial· en· W4386386493 on OpenAlexaff
Martin C. Tammemägi

Bibliographic record

VenueCancer · 2023
Typeeditorial
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsBrock University
FundersNational Cancer Institute
KeywordsMedicineLung cancerCancerLung cancer screeningFamily medicineOncologyInternal medicine

Abstract

fetched live from OpenAlex

Multiple lines of reasoning and evidence, including those provided in the study by Meza and colleagues in this issue, have converged to indicate that using the ≤15 quit‐years rule for determining lung cancer screening eligibility should be abandoned.

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)0.018
metaresearch head score (Gemma)0.064
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.021
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.064
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0070.005
Open science0.0050.001
Research integrity0.0210.039
Insufficient payload (model declined to judge)0.0110.014

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 head0.403
Teacher spread0.380 · 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
GenreEditorial

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

Citations7
Published2023
Admission routes1
Has abstractyes

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