Bibliographic record
Abstract
The paper in this issue by Chambers et al on waiting times for heart surgery in the South Thames region1 is apposite in the current political climate, where the delay to surgery has become a priority and an issue for debate. It is a strange irony that only a decade ago it was not uncommon for German patients to be sent to the UK for heart surgery because waiting times in Germany were so long, and that patients in the UK are now on the verge of being sent to Germany for the same reason. The waiting times reported by Chambers et al1 for the year 1996 were long – but perhaps not as long as might have been anticipated by any of us working in the front line of clinical medicine. The overall waiting times included Accident and Emergency cases and the times for outpatient cases were considerably longer, on average just under a year. There was, moreover, a considerable range in waiting times reflecting variability, and my own experience of even longer waiting times may be part of this. The difficulties encountered in obtaining reliable data are noted and surprising. The sample comprised only 42% of the total number operated on that year. This may have been a source of error but, in the absence of any obvious systematic bias, it can only be assumed that the sample was representative. Any published information about waiting times must, however, be interpreted in this light. The political preoccupation is with reducing waiting times and achieving set targets. The recent cases reported by the Press of waiting list manipulations by NHS employees suggest this preoccupation may have inadvertently led to patients waiting longer than otherwise necessary. There is also a danger that high-risk patients or urgent cases wait an inappropriately long time, because they are displaced in the queue by 'long waiters' for whom there is a penalty if they are allowed to wait more than a set time, usually a year or 18 months. There was no evidence in the study1 that this was the case but there is plenty of room for speculation where waiting times correlated only weakly with clinical need and high priority urgent cases were waiting up to six months. The question arises: should we have a scoring system to help prioritise waiting lists? The Birmingham scoring system is based on clinical parameters and risk of death, whereas the New Zealand system also includes nonmedical parameters reflecting the impact of the illness on the patients' life and on family members. The scores are good indicators of clinical need and intuitively one would accept their adoption into clinical decisionmaking. As far as easily defined endpoints of death and myocardial infarction are concerned, there are few prospective data to validate their use. Indeed, the authors' opening statement1 emphasises the unpredictable nature of coronary artery disease. If the outcomes are truly unpredictable, then the observation that more than 50% of deaths on the waiting lists occur within six weeks of listing is even more alarming. This being so, every effort should be made to have every case operated on within this period of time, regardless of clinical score. Further pressure on waiting lists is likely to arise from changes to management strategies for unstable angina and acute coronary syndromes. The current evidence based on recent published studies indicates that high-risk patients are better managed by an interventional approach within the first few weeks of presentation. In the study by Chambers et al,1 the mean waiting time to surgery for emergency admissions was 160 days. Since these patients presented to the A&E department with chest pain, and coronary angiography was considered necessary, it is a reasonable assumption that a high proportion of them were high risk. There is, indeed, considerable scope for improvement.
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 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 | 0.014 | 0.245 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".