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Record W2315128025 · doi:10.1186/s12998-016-0093-z

Nonfunctioning pituitary macroadenoma: a case report from the patient perspective

2016· article· en· W2315128025 on OpenAlexaff
Craig Bauman, James Milligan, Tammy Labreche, John J. Riva

Bibliographic record

VenueChiropractic & Manual Therapies · 2016
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsUniversity of WaterlooMcMaster UniversityCentre for Family Medicine
FundersNCMIC Foundation
KeywordsMedicineHypopituitarismHeadachesReferralDifferential diagnosisEndocrine systemLibidoPediatricsBlood testInternal medicineSurgeryPathologyHormone

Abstract

fetched live from OpenAlex

BACKGROUND: Nonfunctioning pituitary macroadenoma (NFPA) is a tumour of the endocrine system that is virtually always benign and can be difficult to detect. This case report is presented from the patient's perspective to highlight experiences that led to the eventual diagnosis of this condition. CASE PRESENTATION: A 48 year-old male experienced prolonged and unexplained reduced athletic performance worsening over five years. The patient reported decreased libido, which initiated a testosterone blood test. This confirmed reduced testosterone levels and resulted in an endocrinology referral. A subsequent dynamic contrast MRI of the pituitary region revealed a mass. The most frequent symptoms of NFPA are visual field defects, headaches and features of hypopituitarism (includes fatigue, dizziness, dry skin, irregular periods in women and sexual dysfunction in men). CONCLUSION: Clinicians should consider this differential diagnosis in middle-aged athletes with diminished athletic performance from an unknown cause, test visual fields and inquire if symptoms of headaches or hypopituitarism are present.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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 designCase report
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
Published2016
Admission routes1
Has abstractyes

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