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Record W4386776604 · doi:10.1016/j.heliyon.2023.e20238

Utility of intraoperative pathology consultations of whipple resection specimens and their impact on final margin status

2023· article· en· W4386776604 on OpenAlexaff
N Sina, Ekaterina Olkhov‐Mitsel, Lina Chen, Paul J. Karanicolas, P. Sreedharan Roopchand, Corwyn Rowsell, Tra Truong

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsSt. Michael's HospitalHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsPancreaticoduodenectomyMedicineWhipple ProcedureMargin (machine learning)Resection marginFrozen section procedureSurgical marginSurgeryPancreatectomyRetrospective cohort studyResectionGeneral surgery

Abstract

fetched live from OpenAlex

The resection margin status is a significant surgical prognostic factor for the long-term outcomes of patients undergoing pancreaticoduodenectomy (Whipple procedure). As a result, surgeons frequently rely on intraoperative consults (IOCs) involving frozen sections to evaluate margin clearance during these resections. Nevertheless, the impact of this practice on final margin status and long-term outcomes remains a topic of debate. This study aimed to assess the impact of IOCs on the clearance rate of resection margins following Whipple procedure and distal pancreatectomy. A retrospective database review of all patients who underwent Whipple procedure or distal pancreatectomy at our institution between 2018 and 2020 was performed to evaluate the utility of IOCs by gastrointestinal surgeons and its correlation with final postoperative surgical margin status. A significant variation in the frequency of IOC requests for margins among surgeons was noted. However, the use of frozen section analysis for intraoperative margin assessment was not significantly associated with the clearance rate of final post-operative margins. More frequent use of IOC did not result in higher final margin clearance rate, an important prognostic factor following Whipple procedure.

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 score0.147
Threshold uncertainty score0.490

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 head0.070
GPT teacher head0.393
Teacher spread0.323 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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