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Record W3073452506 · doi:10.2217/lmt-2020-0022

Biopsy frequency and complications among lung cancer patients in the United States

2020· article· en· W3073452506 on OpenAlexfundno aff
Yichen Zhang, Lizheng Shi, Michael Simoff, Oliver J. Wagner, J. Lavin

Bibliographic record

VenueLung Cancer Management · 2020
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersIntuitive
KeywordsMedicineLung cancerBiopsyIntensive care medicineRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to describe the frequency and distribution of biopsy procedures for patients diagnosed and treated for primary lung cancer. STUDY DESIGN: Retrospective cohort study within an administrative database. MATERIALS & METHODS: Databases between 2013 and 2015. RESULTS: The total number of lung biopsies performed among eligible subjects was 32,814; an average of 1.7 biopsies per patient. Bronchoscopy and percutaneous approaches accounted for 95% of all procedures. Complication rates by procedure are remarkably similar irrespective of biopsy frequency. CONCLUSION: Nearly half (46%) of patients in this population experienced multiple biopsies prior to diagnosis. Further, biopsy choice or sequence in patients receiving multiple procedures was unpredictable.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.303
Teacher spread0.288 · 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 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

Citations42
Published2020
Admission routes1
Has abstractyes

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