Determining the time-delay of a mass spectrometry-based tissue sensor
Bibliographic record
Abstract
Breast cancer commonly requires surgical treatment. A procedure used to remove breast cancer is lumpectomy, which removes a minimal healthy tissue margin surrounding the tumor, called a negative margin. A cancer-free margin is difficult to achieve because tumors are not visible or palpable, and the breast deforms during surgery. One notable solution is Rapid Evaporative Ionization Mass Spectrometry (REIMS), which differentiates tumor from healthy tissue with high accuracy from the vapor generated by the surgical cautery. REIMS combined with navigation could detect where the surgical cautery breaches tumor tissue. However, fusing position tracking and REIMS data for navigation is challenging. REIMS has a time-delay dependent on a series of factors. Our objective was to evaluate REIMS time-delay for surgical navigation. The average time-delay of REIMS classifications was measured by video recording. Incisions and corresponding REIMS classifications were measured in tissue samples. We measured the time-delay between physical incision of the tissue and tissue classification. We measured the typical timing of incisions by tracking the cautery in five lumpectomy procedures. The average REMIS time delay was found to be 2.1 ± 0.36 s (average ± SD), with a 95% confidence interval of 0.08 s. The average time between incisions was 2.5 ± 0.87 s. In conclusion, the variation in REIMS tissue classification time-delay allows localization of the tracked incision where the tissue sample originates. REIMS could be used to update surgeons about the location of cancerous tissue with only a few seconds of delay.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".