Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the context of metabolic bone disorders, obtaining biopsies of iliac bone can be useful for establishing a diagnosis in an individual patient or for investigating pathomechanisms when a series of samples is examined. Although bone specimens are usually decalcified for routine pathology to facilitate sample processing, when investigating metabolic bone disorders it is usually much more informative to analyse undecalcified samples. Biopsy samples can be assessed qualitatively and quantitatively. Quantitative analysis by computerised histomorphometry of undecalcified bone biopsy samples is a key tool for studying bone metabolism and, to a lesser extent, bone mass and structure. Standard histomorphometric analyses focuses on trabecular bone and therefore mainly provides information on trabecular remodelling. Remodelling activity changes markedly with age during development. This has to be taken into account when histomorphometry is used in the paediatric setting. Children and adolescents with severe bone fragility should have a bone biopsy for diagnostic purposes unless the diagnosis is obvious from non-invasive examinations. Quantitative histomorphometric analysis of transiliac bone biopsy samples is especially valuable in clinical studies, as this method provides safety and efficacy data that can not be obtained in any other way.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it