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Record W2933665349 · doi:10.21037/atm.2019.03.59

Diagnosis and management of peripheral lung nodule

2019· review· en· W2933665349 on OpenAlex
Taha Khan, Yasir Usman, Tony Abdo, Fawad Chaudry, Jean I. Keddissi, Houssein Youness

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.

Bibliographic record

VenueAnnals of Translational Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsBrock University
Fundersnot available
KeywordsMedicineNodule (geology)RadiologyRadiographyModalitiesSolitary pulmonary noduleMalignancyLungBiopsyComplicationSurgeryPathologyInternal medicineComputed tomography

Abstract

fetched live from OpenAlex

A solitary pulmonary nodule (SPN) is a well-defined radiographic opacity up to 3 cm in diameter that is surrounded by unaltered aerated lung. Frequently, it is an incidental finding on chest radiographs and chest CT scans. Determining the probability of malignancy is the first step in the evaluation of SPN. This can be done by looking at specific risk factors and the rate of radiographic progression. Subsequent management is guided by the type of the nodule. Patients with solid nodules and low pretest probability can be followed radiographically; those with high probability, who are good surgical candidates, can be referred for surgical resection. When the pretest probability is in the intermediate range additional testing such as biopsy should be done. Various modalities are now available to obtain tissue diagnosis. These modalities differ in their yield and complication rate. Patients with SPN should be well informed of each approach's risks and benefits and should be able to make an informed decision regarding the different diagnostic and therapeutic modalities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.863
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.157
GPT teacher head0.438
Teacher spread0.281 · 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