Bibliographic record
Abstract
Dear Editor,I would like to compliment Dr. Austin, et al., for their article titled Laparoscopic Ovariohysterectomy in Nine Dogs (J Am Anim Hosp Assoc2003;39:391–396), in which they describe a new, minimally invasive technique for the sterilization of female dogs.Dr. Austin and her colleagues state that ovariohysterectomy is a necessity for many of our domestic animals, aiding in population control, disease prophylaxis, therapeutics, and behavior modification. In my opinion, this statement is incorrect. These objectives can be accomplished by performing a simple ovariectomy, rather than the more invasive, full ovariohysterectomy. There is no indication to perform an ovariohysterectomy in healthy, nongravid bitches.12 Since many of the complications seen with ovariohysterectomies can be attributed to the cervical stump and the transection of the broad ligament,34 many now advocate ovariectomy as the procedure of choice for the sterilization of female dogs.Since the authors’ goal is to explore minimally invasive techniques for the sterilization of female dogs, I would like to hear their arguments supporting the use of a full ovariohysterectomy on healthy, nongravid animals instead of the less-invasive ovariectomy procedure. In my opinion, minimally invasive surgery should start with limiting the surgical trauma to the least amount necessary.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".