False Negative Sentinel Lymph Node Biopsies in Melanoma May Result From Deficiencies in Nuclear Medicine, Surgery, or Pathology
Bibliographic record
Abstract
OBJECTIVE: To investigate a cohort of melanoma patients with false negative (FN) sentinel node (SN) biopsies (SNBs) to identify the reasons for the FN result. SUMMARY OF BACKGROUND DATA: SNB is a highly efficient staging method in melanoma patients. However, with long-term follow-up FN SNB results of up to 25% have been reported. METHODS: Seventy-four SNs from 33 patients found to have had an FN SNB were analyzed by reviewing the lymphoscintigraphy, surgical data, and histopathology, and by assessing nodal tissue using multimarker real-time quantitative reverse transcription (qRT) polymerase chain reaction, and antimony concentration measurements (as a marker of "true" SN status) using inductively coupled plasma mass spectroscopy. RESULTS: Nine SNs (12%) from 9 patients (27%) had evidence of melanoma on histopathologic review. Twelve SNs (16%) from 10 patients (30%) were qRT(+). Four of these 12 SNs were positive on histopathology review and 8 were negative. Four patients (12%) were upstaged by qRT. Sixteen patients had their SNB histology, lymphoscintigraphy, and surgical data reviewed. Identifiable causes of the FN SNBs were not found after review of all modalities in 4 patients. SNs from all 4 patients had antimony levels indicative of an SN. Of the SNs evaluable by qRT, 1 was qRT(+) and 7 SNs from 2 patients were qRT(-). CONCLUSIONS: An FN SN can occur because of deficiencies in nuclear medicine, surgery, or pathology. qRT can detect "occult" metastatic melanoma in SNs that have been identified as negative by histopathology.
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 | 0.004 |
| 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) | 0.001 | 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".