Cortisol and prolactin concentrations during repeated blood sample collection from freely moving, mouse-sized mammals (Phodopus spp.).
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Validation of a method for obtaining blood samples that does not change cortisol or prolactin concentrations yet allows serial blood samples to be collected from animals under anesthesia, without prior handling, from freely interacting social groups of small mammals. METHODS: Results from five experiments are reported. Male dwarf hamsters (Phodopus spp.) were housed in modified home cages under continuous flow of compressed air that could be switched to isoflurane in O2 vehicle without approaching the cages. RESULTS: Dwarf hamsters respond to manual restraint with behavioral distress and increase in the concentration of the dominant glucocorticoid, cortisol, and decrease in prolactin concentration. Both effects are evident within one minute. In contrast, when this new method was used, neither cortisol nor prolactin changed in response to repeated sample collection (up to 8 successive samples at 2 hour intervals), prolonged isoflurane exposure, or substantial blood volume reduction (30%). Prolactin concentration was suppressed and cortisol concentration was increased in response to stimuli from other hamsters tested without anesthesia. Suppression of prolactin concentration was graded in response to the degree of stress and equaled the pharmacologic reduction caused by bromocryptine mesylate (50 microg of CB154 x 3 days). CONCLUSIONS: The technique is superior to alternatives for studies of behavioral endocrinology of freely interacting small mammals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".