Bibliographic record
Abstract
A 77-year-old farmer with recurring kidney stones visits his urologist for an annual examination. Prior to seeing the patient, the physician is taken aside by her nurse, who tells her the patient had been in the emergency department the previous night with hematuria. A CAT scan had been done, which indicated that the renal tumor seen on last year's CAT scan was larger and there were now lung metastases. The physician cannot remember ever seeing the radiology report from last year. To her complete surprise, it is found filed in the patient's chart. There is no record in the chart that the results were ever shared with the patient. She considers herself extremely meticulous and has never had such an oversight before. The urologist considers what she should tell the patient. A 12-year-old boy has cataract surgery at a large teaching hospital. At a critical moment the surgeon's hand slips, severely rupturing the lens capsule. The planned implantation of an intraocular lens has to be abandoned. Instead, the patient will have to use a contact lens. The physician wonders what he should tell the patient and his family about the surgery. What is medical error? Well-publicized reports of harm occurring to patients as a result of their medical care in the USA (Patient Safety Foundation, 1998), Canada (Sinclair, 1994) and the UK (Smith, 1998) have raised public concerns about the safety of modern healthcare.
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 | 0.021 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".