Bibliographic record
Abstract
Guidelines are often promulgated by specialists and special interests without regard to the fact that the adoption of any guideline is balanced by the reduction of resources, especially time, for other care. There is not enough time in a day to follow all the guidelines. 1,2 Given that sobering consideration, let’s look at this issue’s article on ‘Canadian CT Head Rule adherence in a rural hospital without in-house CT’. At that hospital, it was ‘only’ 35%. I like the Canadian CT Head Rule (CCHR). When I worked in rural hospitals without an in-house CT, the rule was particularly useful in enabling the transfer of a patient who I wanted, sometimes desperately, elsewhere. However, I confess that that rule was quite selectively used. This lack of ‘adherence’ is not just in Canada; a quick look at the literature reveals the use of CT in minor head injuries in a New Zealand rural hospital at 22%. 3 So, is this bad? An excess of urban and specialised care guidelines can lead to the belief that rural generalist physicians offer inferior care. This is a misconception that even some rural generalists subscribe to. There is no evidence for it. In regard to the guideline in question, it was generated by ten large Canadian urban hospitals staffed with inexperienced residents and students. 4 They had a pre-existing high rate of (in-house) CT head for a minor head injury. The CCHR successfully reduced the CT rate without increasing missed cases. So, how relevant is the rule for small rural hospitals mostly staffed with experienced generalist physicians without an in-house CT? In such settings, applying the CCHR would dramatically increase the number of CTs, and it is still untested if this would improve clinical outcomes. Let’s think of the resources involved. CT, of course, but also time and transfer. Some patients really don’t want to leave town, especially for a ‘minor’ head injury. Ultimately, we cannot be held to guidelines that do not apply to us. Clinical judgment will lead us to weigh the issues that need addressing in our admittedly complex patients. Always, we need to prioritise for clinical reasons, respecting the values of our patients. Sometimes, we will apply guidelines. Sometimes, we will not. We should not be browbeaten for it.
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 | 0.237 | 0.613 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.029 | 0.051 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".