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Record W2791164993 · doi:10.1002/jso.25003

Is tissue still the issue? Lobectomy for suspicious lung nodules without confirmation of malignancy

2018· article· en· W2791164993 on OpenAlexaff
Suha Kaaki, Biniam Kidane, Sadeesh Srinathan, Lawrence Tan, Gordon Buduhan

Bibliographic record

VenueJournal of Surgical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsMedicineMalignancyBiopsyIncidence (geometry)Retrospective cohort studyLungSurgeryNodule (geology)CohortRadiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Histologic confirmation of malignancy has been indicated for a suspicious lung nodule prior to resection. The purpose of this study was to determine whether or not foregoing routine tissue biopsy increased the incidence of lobectomy for benign lesions. METHODS: Retrospective cohort of 256 patients who underwent thoracoscopic or open lobectomy for a confirmed or suspected pulmonary malignancy, with or without tissue diagnosis. Clinical, radiographic, and pathologic data were compared. RESULTS: Among 256 patients, 127 had attempted biopsy (group A) and 129 had no biopsy procedure (group B). There was no significant difference in the incidence of benign resections between the groups (Group A = 4 (3.2%) benign pathology vs group B = 9 (7.0%; P = 0.16). Group B had significantly lower operative time (127.1 vs 112.3 minutes; P = 0.004) and intraoperative complications (23 vs 37 patients; P = 0.03). There was a trend toward longer hospital stay and surgical waiting time in group A (6.6 vs 5.2 days, P = 0.24; 92.4 vs 66.2 days; P = 0.14, respectively). CONCLUSION: Foregoing biopsies and proceeding to lobectomy in selected patients with suspicious lung nodules is safe, did not increase the incidence of resected benign pathology, and may decrease surgical wait time. Patients should be carefully evaluated and counseled.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.377
Teacher spread0.354 · 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 designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

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