Bibliographic record
Abstract
SIR—We thank Dr Chalcroft for his comments relating to our paper [3]. The categories of the Reported Edmonton Frail Scale (REFS) to determine frail and non-frail patients were reported under the sub-heading of ‘Data Analysis’ in the Methods section of the paper. The cut-off scores to determine frailty remained the same as those used in the original Edmonton Frail Scale [4] with not frail (0–5) and apparently vulnerable (6–7) making up the ‘non-frail’ group and the mildly frail (8–9), moderately frail (10–11) and severely frail (12+) making up the ‘frail’ group. The groups were combined in order to maximise the power of the data to find differences, particularly when undertaking survival analysis to determine key outcomes (stroke, haemorrhage, death). The concept of frailty is a hotly debated area. Although its use for identifying vulnerable patients is unquestioned, a clear definition that captures the social, physical and psychological changes associated with frailty has not been developed yet. The multidimensional nature of frailty and the struggle to untangle it from other concepts such as disability and comorbidity make the development of a scale to measure the syndrome a difficult task [1]. However, in order to investigate frailty and its influence on patient management, studies are restricted to using currently available definitions.
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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".