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Record W2799990614 · doi:10.14740/jh375w

Retrospective Study of High Hemoglobin Levels in 56 Young Adults

2018· article· en· W2799990614 on OpenAlexaffvenueabout
Alexandra Desnoyers, Michel Pavic, Paul-Michel Houle, Jean‐François Castilloux, Patrice Beauregard, Line Delisle, Richard Le Blanc, Jean Dufresne, Josie-Anne Boisjoly, Vincent Éthier

Bibliographic record

VenueJournal of Hematology · 2018
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineHematocritEtiologyRetrospective cohort studyPhlebotomyInternal medicinePopulationHemoglobinCohortAspirinGastroenterologySurgeryPediatrics

Abstract

fetched live from OpenAlex

BACKGROUND: Erythrocytosis is a frequent request for consultation in the hematologic field. The diagnostic approach is well established in the general population but in a young adult, finding the etiology of erythrocytosis can be a real diagnostic challenge. METHODS: This is an observational retrospective unicentric study made at the Sherbrooke University Hospital Center, over a period of 20 years (1995 - 2015). Every patient aged between 16 and 35 years old with a significant elevation of hemoglobin or hematocrit was included (hemoglobin > 185 g/L and/or hematocrit > 0.52 in men; hemoglobin > 165 g/L and/or hematocrit > 0.48 in women). RESULTS: mutation and serum EPO dosage were performed in 17.9% and 23.2% of cases respectively. Seven patients were treated with aspirin and five patients had phlebotomies. CONCLUSIONS: This retrospective study reveals an actual clinical management that is often discordant with the current recommendations and a frequent lack of follow-up after initial investigations. Harmonization of management of erythrocytosis appears to be highly desirable.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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".

Quick stats

Citations6
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueJournal of HematologySame topicMyeloproliferative Neoplasms: Diagnosis and TreatmentFrench-language works237,207