Effects of preoperative administration of ketoprofen on whole blood platelet aggregation, buccal mucosal bleeding time, and hematologic indices in dogs undergoing elective ovariohysterectomy
Bibliographic record
Abstract
OBJECTIVE: To determine effects of preoperative administration of ketoprofen on whole blood platelet aggregation, buccal mucosal bleeding time, and hematologic indices in dogs after elective ovariohysterectomy. DESIGN: Randomized, masked clinical trial. ANIMALS: 22 healthy dogs. PROCEDURE: 60 minutes before induction of anesthesia, 11 dogs were given 0.9% NaCl solution (control), and 11 dogs were given ketoprofen (2 mg/kg [0.9 mg/lb], IM). Thirty minutes before induction of anesthesia, glycopyrrolate (0.01mg/kg [0.005 mg/lb]), acepromazine (0.05 mg/kg [0.02 mg/lb]), and butorphanol (0.2 mg/kg 10.09 mg/lb]) were given IM to all dogs. Anesthesia was induced with thiopental (5 to 10 mg/kg [2.3 to 4.5 mg/lb], IV) and maintained with isoflurane (1 to 3%). Ovariohysterectomy was performed and butorphanol (0.1 mg/kg [0.05 mg/lb], IV) was given 15 minutes before completion of surgery. Blood samples for measurement of variables were collected at intervals before and after surgery. RESULTS: In dogs given ketoprofen, platelet aggregation was decreased 95 +/- 10% and 80 +/- 35% (mean +/- SD) immediately after surgery and 24 hours after surgery, respectively, compared with preoperative values. At both times, mean values in dogs given ketoprofen differed significantly from those in control dogs. Significant differences between groups were not observed for mucosal bleeding time or hematologic indices. CONCLUSIONS AND CLINICAL RELEVANCE: Preoperative administration of ketoprofen inhibited platelet aggre gation but did not alter bleeding time. Ketoprofen can be given before surgery to healthy dogs undergoing elective ovariohysterectomy, provided that dogs are screened for potential bleeding problems before surgery and monitored closely after surgery.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".