An Interference That Makes You Blue?
Bibliographic record
Abstract
A 66-year-old female with a history of smoking, lung adenocarcinoma, and chronic obstructive pulmonary disease presented with dyspnea and light-headedness for 4 months. She was cyanotic on examination and self-reported increased carboxyhemoglobin in the past. Co-oximetry using 2 separate blood gas instruments at 2 separate time points revealed methemoglobin results that were suppressed due to an interfering substance (in one case) or flagged (in the second case) due to increased sulfhemoglobin (Table 1). Carboxyhemoglobin was elevated at the second time point (Table 1). She was treated with methylene blue after admission. Laboratory results from co-oximetry measurements. aCredited: interfering substance present. bSulfhemoglobin interference detected. Laboratory results from co-oximetry measurements. aCredited: interfering substance present. bSulfhemoglobin interference detected. What was the cause of methemoglobinemia in this patient? Are sulfhemoglobin and carboxyhemoglobin elevated in this patient, and why? How do you obtain an accurate measurement of methemoglobin in the presence of interference? The answers are below. Inherited methemoglobinemia is caused by cytochrome B5 reductase deficiency or hemoglobin M disease (1). This patient had hemoglobin M Saskatoon, which can lead to falsely elevated sulfhemoglobin and carboxyhemoglobin results due to spectral interference using co-oximetry (2). Accurate methemoglobin measurements can be achieved using direct methods such as Evelyn-Malloy (3, 4). Direct measurement of methemoglobin and sulfhemoglobin collected later on day 2 resulted at 10.4% and 0%, respectively. Baseline methemoglobin for hemoglobin M Saskatoon patients can range from 0.5% to 14.9% (4). The corresponding author takes full responsibility that all authors on this publication have met the following required criteria of eligibility for authorship: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects of the article thus ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Nobody who qualifies for authorship has been omitted from the list. Ruth Melka (Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Investigation-Equal, Methodology-Equal, Visualization-Equal, Writing—original draft-Equal, Writing—review & editing-Equal), Christopher Farnsworth (Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Investigation-Equal, Methodology-Equal, Resources-Equal, Writing—review & editing-Equal), and Yanchun Lin (Conceptualization-Equal, Data curation-Equal, Formal analysis-Equal, Investigation-Equal, Methodology-Equal, Project administration-Equal, Resources-Equal, Supervision-Equal, Writing—original draft-Equal, Writing—review & editing-Equal). Upon manuscript submission, all authors completed the author disclosure form. None declared. C.W. Farnsworth declares receipt of research funding from Roche, Abbott, Siemens, Beckman Coulter, Cepheid, and Biomerieux and consulting fees from Abbott, Werfen, and Cytovale. C.W. Farnsworth is SYCL Liaison for 2024–2025 for Clinical Chemistry, ADLM.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".