Laboratory reference intervals influence referral patterns for hemoglobin abnormalities in the Ontario virtual care system
Bibliographic record
Abstract
This retrospective cross-sectional study investigates the impact of laboratory-specific hemoglobin reference intervals on electronic consultation (eConsult) referral patterns for suspected anemia and elevated hemoglobin at a tertiary care center in London, Ontario that serves Southwestern Ontario. The study analyzed referrals through the Ontario Telemedicine Network's eConsult platform for hemoglobin abnormalities, excluding patients under 18 years old, between July 1, 2019, and June 30, 2023.The main outcome measures were influence of hemoglobin reference intervals on the referral patterns for suspected anemia and elevated hemoglobin, as well as the extent of pre-referral laboratory testing. Of the 619 eConsults reviewed, 251 referrals for suspected anemia and 93 for elevated hemoglobin were analyzed. Referral patterns showed significant variance in hemoglobin levels based on different laboratory thresholds. Referrals for suspected anemia in females from laboratories whose lower limit was 120 g/L or greater had a hemoglobin concentration 7.5 g/L greater than referrals that used laboratories with a threshold lower than 120 g/L. The study also identified potential areas for improvement in pre-referral investigations; 44% of eConsults did not provide a ferritin level, 53% were missing a B12 level, and 81% were missing a reticulocyte count. In conclusion, laboratory reference intervals for hemoglobin significantly influence referral patterns for suspected hemoglobin abnormalities in Ontario's eConsult system. There is a need for standardized reference intervals and comprehensive pre-referral testing to avoid unnecessary medicalization and referrals. We propose an anemia management algorithm to guide primary care providers in the pre-referral investigation process.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".