Bibliographic record
Abstract
hypothyroidism is an important distraction and a possible cause of various symptoms, perhaps including the chest pain.In Scotland (population about 5 million), only 258 women under 45 had coronary heart disease diagnosed in 2004.4 If these women were evenly distributed, an average general practitioner would only see one presentation every 20 years or so.But they are not evenly distributed.I have colleagues from parts of Glasgow who would not in the least be surprised by the correct diagnosis.The likelihood of coronary heart disease is much higher in deprived communities, independent of traditional risk factors.5 For general practitioners in different places, our experiences-and consequent diagnostic practices-are quite different.We don't know how Mrs Patel's general practitioner responded to her chest pain and hypothyroidism.We know she received treatment for a presumed musculoskeletal cause, but I wonder if her thyroxine treatment was cautious (25 g is an unusually low dose) because coronary heart disease was also being considered.Not unusually, Mrs Patel re-presented with chest pain six months after angioplasty.A clinical diagnosis is now even more difficult.Even for "experienced" angina patients, it can be impossible to differentiate types of chest pain, causing some patients disabling anxiety and others to delay presentation with critical ischaemia.This time the pain was not related to exertion and, thankfully, investigations were normal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".