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Record W4251892418 · doi:10.1093/ajcp/aqx141

Endocrine Pathology

2017· article· en· W4251892418 on OpenAlexaboutno aff
Syed A. Hoda, Rana S. Hoda

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2017
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsPathologyEndocrine systemMedicineInternal medicineHormone

Abstract

fetched live from OpenAlex

“The heart has its reasons, and the endocrines have theirs.” — Aldous Huxley, in Island Endocrine diseases are complicated. Many of these diseases affect several organs. Some have familial connotations; a few are diagnosed biochemically; and others are identified only through biopsy. Aggressive-appearing lesions may behave in an innocuous manner, and bland-looking neoplasms can metastasize. Regardless of our current ability (or inability) to understand all the reasons of the endocrines, appropriate pathologic testing and precise diagnosis remain central to their management. The aptly entitled Endocrine Pathology is divided into 30 chapters that are arranged in three parts. The first part—the shortest—provides a summary of the clinical aspects of endocrine diseases (and includes several informative algorithms). The second part covers the investigative techniques utilized to study endocrine disorders and organs (including cytological and intraoperative assessments). The third part—the longest—provides wide-ranging coverage of the anatomical (mainly organ-based) aspects of endocrine disorders. The exhaustive coverage in this book is provided by 62 multinational authors—most of whom are based in Canada and Turkey. Some notable experts in the field are among the contributors. Not surprising, the thyroid gland secures the most coverage. In general, other organs get coverage mostly appropriate to their prevalence and importance. There are other chapters that deal with seemingly esoteric topics such as “the brain as an endocrine organ” and even “soft tissue in endocrine disease.” The chapter “Morphology and Immunohistochemistry” ought to be considered essential reading for the surgical pathologist-in-training. This chapter contains rational reminders (eg, “microscopy is the foundation of modern pathology”). Practical pointers are present elsewhere (eg, “any parathyroid gland that weighs more than 60 mg and measures more than 1.0 cm should be considered an abnormally enlarged gland”). Our understanding of endocrine diseases, particularly of their molecular aspects, is evolving briskly. Thus, there are sections entitled “Molecular Anatomy” in several chapters that impart potentially useful leading-edge information (eg, “cardiac hypertrophy is associated with re-expression of genes for fetal proteins…”). The text is mostly cogent. Some statements may confuse the novice (eg, “microfollicles are not simply small follicles”). Numerous illustrations effectively complement the text, although some images are superfluous (eg, accessioning of a frozen section specimen), and a few could have benefited from a more descriptive legend (eg, Figure 13.72, depicting vascular invasion in thyroid neoplasms). Endocrine Pathology has been promoted by its publishers as a “comprehensive and up-to-date book” and “the definitive resource on endocrine pathology for all pathologists, endocrinologists, and researchers.” This claim appears, for the most part, to be valid because the book effectively covers the myriad biochemical, clinical, cytopathological, familial, histopathological, immunohistochemical, immunological, radiological, molecular, and several additional aspects of nonneoplastic and neoplastic diseases that involve the hormone-producing organs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.174
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0060.003
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.1740.136

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 head0.084
GPT teacher head0.496
Teacher spread0.413 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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