Accuracy and precision of metered doses of nitroglycerin lingual spray
Bibliographic record
Abstract
PURPOSE: The accuracy and precision of nitroglycerin lingual spray are examined. METHODS: Two hundred and twenty-four bottles of nitroglycerin lingual spray product (0.4 mg nitroglycerin per dose, 75 doses per bottle) were collected from the hospital pharmacy at Vancouver General Hospital. Bottles were placed upright on a level surface. After three priming sprays, each bottle was weighed and an initial measurement was recorded. All measurements were recorded in grams to three decimal places and the bottles were weighed on an analytical balance. Three more sprays were made to simulate those in the patient instruction insert. The scale was set to zero before each measurement, and the bottles were then placed on the scale for five seconds before the final weight was recorded. The three spray weights were averaged to obtain a mean spray weight for each bottle. RESULTS: The manufacturer guarantees a minimum volume of 0.05-0.1 mL per spray. The lower value of 0.05 mL was used for comparison. The weight of 0.05 mL of preparation was calculated, and this was compared to the mean spray weight of all samples tested. A single sample t test revealed a significant difference between the value reported by the manufacturer and the mean of the sample (t((224)) = -23.42, p < 0.0005). CONCLUSION: A significant difference was observed between the expected weight of nitroglycerin solution per metered dose and the mean weight of the study samples. Results of the study indicate that dose delivery may be inaccurate but is fairly precise.
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".