Sensitivity of commercial scanners to microchips of various frequencies implanted in dogs and cats
Bibliographic record
Abstract
OBJECTIVE: To evaluate the sensitivity of 4 commercially available microchip scanners used to detect or read encrypted and unencrypted 125-, 128-, and 134.2-kHz microchips under field conditions following implantation in dogs and cats at 6 animal shelters. DESIGN: Cross-sectional study. Animals-3,949 dogs and cats at 6 animal shelters. PROCEDURES: Each shelter was asked to enroll 657 to 660 animals and to implant microchips in 438 to 440 animals (each shelter used a different microchip brand). Animals were then scanned with 3 or 4 commercial scanners to determine whether microchips could be detected. Scanner sensitivity was calculated as the percentage of animals with a microchip in which the microchip was detected. RESULTS: None of the scanners examined had 100% sensitivity for any of the microchip brands. In addition, there were clear differences among scanners in regard to sensitivity. The 3 universal scanners capable of reading or detecting 128- and 134.2-kHz microchips all had sensitivities > or = 94.8% for microchips of these frequencies. Three of the 4 scanners had sensitivities > or = 88.2% for 125-kHz microchips, but sensitivity of one of the universal scanners for microchips of this frequency was lower (66.4% to 75.0%). CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that some currently available universal scanners have high sensitivity to microchips of the frequencies commonly used in the United States, although none of the scanners had 100% sensitivity. To maximize microchip detection, proper scanning technique should be used and animals should be scanned more than once. Microchipping should remain a component of a more comprehensive pet identification program.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".