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Record W2028523859 · doi:10.4103/2303-9027.127118

Endoscopic ultrasound-guided fine-needle aspiration: Getting to the point

2014· article· en· W2028523859 on OpenAlexaff
AnandV Sahai

Bibliographic record

VenueEndoscopic Ultrasound · 2014
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsHôpital Saint-Luc
Fundersnot available
KeywordsMedicineMalignancyFine-needle aspirationRadiologyEndoscopic ultrasoundRest (music)SuctionBiopsyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Endoscopic ultrasound (EUS)-guided fine-needle aspiration (FNA) is arguably the cornerstone of the on-going popularity of EUS. The ability to safely and reliably obtain cytological or histological proof of malignancy; or to reliably exclude malignancy in indeterminate lesions is clinically extremely useful — particularly when lesions are otherwise inaccessible. In expert hands, the sensitivity of EUS-FNA for malignancy is over 90% (higher for nodes, lower for pancreatic malignancies).[1] Can we improve on these results? Can less experienced endosonographers be as effective? Finding the best and simplest technique could answer both questions. Most studies on EUS-FNA have focused on comparing variables such as needle sizes, needle type, suction, stylet use, on site cytological analysis, number of passes, etc. These studies seem to ignore the fact that the primary goal of the EUS-FNA technique is to effectively position the FNA needle into the target lesion and then move the needle to collect tumor cells. After all, if the needle does not come in contact with the tumor, it does not matter what other variable you change, there can be no diagnosis of cancer. In other words, these studies ignore what, in practice, is likely the most important variable in EUS-FNA — how and where the EUS-FNA needle is positioned and moved within the target lesion. It is well-known that what appears as a “mass” or malignant node may contain only a small focus of tumor. The rest may be inflammation or necrosis. The only study that compared wide tissue sampling (“fanning”) to regular sampling showed a clear advantage to fanning and a yield after the first pass that was comparable to the results in most other studies after multiple passes.[2] All this is to say that maybe we should be focusing on what is happening at the point of the needle, rather than at the other end! Is it possible that proper technique can overcome all other variables? The current literature does not allow us to answer this question because great majority of papers never describe the needle path (whether the entire lesion traversed, what part of the lesion was sampled) or if fanning was used or not. Every “expert” believes that his or her technique is best and they are very resistant to change. In our experience, we are able to obtain a sensitivity of 90% with only 2 passes, with no stylet, no suction and no cytologist.[3] How is this possible? We believe that it is because we use an aggressive multi-pass fanning technique. Our simplified technique requires less nursing support and is faster and safer (due to no risk of needle stick injury during re-insertion of the stylet). If this basic technique does not work, we may try to use a “salvage” maneuver by adding suction or perhaps trying a different needle type (such a needle with a side hole). Anecdotally, we find this is rarely helpful. All this is to say that there may be many different variables that need to be taken into account maximize the results of EUS-FNA, or maybe just one variable: The endosonographer — because that is the one variable that controls how the needle is used. If this is the case, could training be more important that hardware? In this issue of EUS, we hope to offer a balanced and detailed assessment of as many issues related to maximizing the yield of EUS-FNA; as well maximizing its effectiveness. I would like to sincerely thank our international panel of experts for their thoughtful contributions.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.327
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2014
Admission routes1
Has abstractyes

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