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Record W2622177392

[Hemoglobin changes (Hb) in miners exposed to high altitude and associated factors].

2018· article· en· W2622177392 on OpenAlexaff
Christian R. Mejía, Dante M. Quiñones-Laveriano, Raúl Gómero, L. Pérez-Pérez

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsBarrick Gold (Canada)
Fundersnot available
KeywordsEffects of high altitude on humansAltitude (triangle)DemographyBody mass indexMedicineHemoglobinMultivariate analysisSea levelGeographyInternal medicineMathematicsPhysical geography
DOInot available

Abstract

fetched live from OpenAlex

AIM: To determine the variation of hemoglobin (Hb) in two groups of miners working at different altitudes. METHODOLOGY: A longitudinal study conducted in a private company. Hb was obtained from entrance exams and annual checks of workers at two locations: at sea level and at Peruvian highlands (4,100 m), taken by trained staff and equipment calibrated to environmental conditions. We analyzed variations in the course of the years with the PA-GEE statistical test; p values were obtained. RESULTS: Of the 376 workers, 89% (322) were men, the median age was 32 years (range 20-57) and 84% (304) were at high altitude. In multivariate analysis, male sex (p < 0.001), body mass index (BMI; p = 0.021) and working at high altitude (p < 0.001) were associated with the greatest variation of Hb in time, adjusted for age, length, and type of work. DISCUSSION: These findings should be considered for health surveillance of workers exposed to similar conditions to prevent chronic mountain sickness. CONCLUSION: The change in Hb of miners was associated with male sex, BMI, and work at high altitude.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.225
Teacher spread0.207 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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