Comprehensive hematological reference intervals in a healthy adult male population
Bibliographic record
Abstract
Reference intervals (RIs) are important tools for improving medical decision-making. Hematology reference values can be influenced by important covariates such as genetic and environmental factors, rendering it essential to define RIs for specific populations. Therefore, we aimed to establish accurate and robust RIs for hematological markers in a healthy adult male Iranian population. This cross-sectional study was conducted in a population of 723 males aged 20-60 years old. Hematological parameters were routinely measured using a Sysmex auto analyser system (KX-21 N). The quality of assays was monitored using commercial quality control samples. The nonparametric rank method, as recommended by the Clinical and Laboratory Standards Institute (CLSI) guidelines, was used to calculate the 2.5th and 97.5th percentiles as the lower and upper reference limits, respectively. Of the 12 hematological parameters assessed, only mean platelet volume (MPV) demonstrated significant age-specific differences, requiring two partitions from 20 to 35 years (8.7-12.2 fL) and 35 to 65 years (8.5-11.5 fL). The remaining hematological parameters (e.g. leukocyte, erythrocyte, and platelet parameters) could be defined by one age range. This study established RIs for 12 routinely used hematological parameters in a healthy male population living in the northeastern region of Iran. Established RIs differed from those previously reported by other cohorts, highlighting the importance of population-specific RIs.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".