Determinants of diagnostic delay in autoimmune atrophic gastritis
Bibliographic record
Abstract
BACKGROUND: Autoimmune atrophic gastritis (AAG) is characterised by a wide clinical spectrum that could delay its diagnosis. AIMS: To quantify the diagnostic delay in patients suffering from AAG and to explore possible risk factors for longer diagnostic delay. METHODS: Consecutive patients with AAG evaluated at our gastroenterological outpatient clinic between 2009 and 2018 were included. Diagnostic delay was estimated as the time lapse occurring between the appearance of the first likely symptoms, laboratory alterations, and other clues indicative of AAG and the final diagnosis. Patient-dependent and physician-dependent diagnostic delays were also assessed. Multivariable regression models were fitted. RESULTS: 291 patients with AAG (mean age at diagnosis 61 ± 15 years; F:M ratio = 2.3:1) were included. The median overall diagnostic delay was 14 months (interquartile range [IQR] 4-41). Factors associated with longer median overall diagnostic delay were female sex (17 months, IQR 5-48), having a previous misdiagnosis (36 months, IQR 17-125) and a history of infertility/miscarriages (33 months, IQR 8-120), whereas a higher level of education was associated with longer patient-dependent diagnostic delay (4 months, IQR 1-12). First evaluation by a gastroenterologist was associated with a median longer diagnostic delay (6 months, IQR 2-15) compared to an internist (3 months, IQR 3-31) and a haematologist (1 month, IQR 0-2). Age, socioeconomic or marital status did not affect the diagnostic delay. CONCLUSIONS: AAG is burdened by substantial diagnostic delay, especially in female patients, and due to lack of awareness, particularly among gastroenterologists. Uncommon vitamin B12 deficiency-related manifestations are overlooked and may prolong the diagnostic delay.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".