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Record W4391364307 · doi:10.21037/jtd-23-464

Benign tumors of the chest wall

2024· article· en· W4391364307 on OpenAlexaff
Fabrizio Minervini, Consolato Sergi, Marco Scarci, Peter Kestenholz, Leonardo Valentini, Laura Boschetti, Pietro Bertoglio

Bibliographic record

VenueJournal of Thoracic Disease · 2024
Typearticle
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineRadiological weaponAsymptomaticRadiologyBiopsyChest radiographRadiographySurgery

Abstract

fetched live from OpenAlex

Benign tumors of the chest wall are rare tumors that might arise from all the tissues of the chest: vessels, nerves, bones, cartilage, and soft tissues. Despite benign features, these tumors can have several histological characteristics and different behaviors. Even if they do not influence life expectancy, rarely they may have a potential risk of malignant transformation. They can cause several, oft, unspecific symptoms but more than 20% of affected patients are asymptomatic and are being diagnosed incidentally on chest radiograph or computed tomography scan. Pain is the most common described symptom. Together with a detailed medical history, a rigorous and meticulous clinical and radiological assessment is mandatory. If radiological features are unclear or in case surgery could not be performed, a biopsy should be indicated to establish a diagnosis. Radical surgical resection can often be offered to resect and cure these neoplasms, but this is might not be true for all types of tumors and, in some cases, their dimension or position might contra-indicate surgery. Given the rarity of these tumors, there is a lack of treatment's guidelines and prospective trials that include a significant number of patients. This review discusses, according to the latest evidence, the histological features and the best treatment of several chest wall benign tumors.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.410
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.022
GPT teacher head0.343
Teacher spread0.321 · 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 teacher head, 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

Citations11
Published2024
Admission routes1
Has abstractyes

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