Bibliographic record
Abstract
brown-yellow dot inhaler' regularly.Colour has always been used to aid recognition, this convention is not new in medicine.A standardised colour code for user-applied syringe labels for anaesthetic drugs exist in the US, Australia, New Zealand, South Africa, and Canada. 2 A single standard system for syringe labelling in critical care areas has been adopted in the UK as well.3 There is always a problem in reading the labels as instructions are often written at a level too complex for low literacy patients.4,5,6 Inadequate literacy, without any doubt, is a barrier to asthma knowledge and proper self-care.6,7 Moreover, patients who have a different first language than the healthcare provider can raise additional issues.So there will be a large group of patients who can identify their inhalers only by the colour.Older people who have difficulty identifying colours will have difficulty reading fine print as well and will need assistance.People who are colour blind should continue to read the labels or identify their inhalers by the design or size.We, therefore, believe that adding universal colour dots to the current system will only do good in creating uniformity without causing any additional limitations.
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.082 | 0.018 |
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".