Bibliographic record
Abstract
SIR—We thank Drs Khan and Myers for their comments relating to our paper [1]. We reported that older patients with atrial fibrillation (AF) in acute hospital care were less likely to receive warfarin if they fulfilled frailty criteria. Frailty was defined using a reported version of the Edmonton Frail Scale, a validated scale for use by non-clinicians that assesses cognition, health attitudes and mood, medication use, nutrition, continence, burden of medical illness, social support and functional independence [2]. Furthermore, our study found a significant difference in the rates of embolic stroke and death between patients deemed frail and those deemed non-frail. The study also found that frail patients were more likely to have a haemorrhagic event 3 and 6 months post-discharge. Frailty was associated with age but not directly related to age. In fact frailty was better correlated with disability and co-morbidity than with age. Our findings support the view of Drs Khan and Myers that age alone should not (and does not) determine prescribing of antithrombotic medication for older patients with AF. A previous interventional study in the same hospital acknowledged this issue by specifically excluding age per se from the decision-making process, and instead focussing on medical, functional, cognitive, iatrogenic and social factors affecting the use of antithrombotics [3]. The present study follows from this in evaluating additional factors that may help determine the optimum treatment for older patients with AF. We found that frailty may be a useful risk stratification tool for such patients. Most of the factors that Drs Khan and Myers advocate considering in anticoagulation of older patients would be assessed using the frailty tool that was applied in our study. Differences in the event rates observed between our studies may relate to the populations studied: our participants all had AF, were recruited from acute care wards, were followed over 6 months and were not all anticoagulated; while their participants had a range of conditions, were followed for an average of 3.78 years in the community and were all anticoagulated. It is possible that there was a higher prevalence of frailty in our study (64% of participants) than in the population reported by Drs Khan and Myers, which may contribute to the higher rates of adverse events we observed. Risk stratification tools such as frailty are valuable when prescribing for older patients, who have wide inter-individual variability, and potentially have much to gain from medication as well as a high risk of adverse drug reactions. None declared.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".