Nucleolus vs Nucleus Count for Identifying Spiral Ganglion in Human Temporal Bone
Bibliographic record
Abstract
OBJECTIVES: Spiral ganglion (SG) counting is used in experimental studies conducted on age-, noise-, and drug-induced sensorineural hearing loss, as well as in the assessment of cochlear implant performances. Different methods of counting have been reported, but no definite standardization of such procedure has been published. The aim of our study is to identify the best method to count human spiral ganglions (SGs). MATERIALS AND METHODS: By identification of nuclei or nucleoli as described by Schucknect, seven researchers with different experience levels counted SGs in 123 human temporal bones (TBs). Data on time of post-mortem bone removal post-mortem, methods of specimen's fixation, decalcification, and coloration were collected to test their possible influence on human tissue. Percentage, two-tailed t-test, Spearman's test, and one-way ANOVA were used to analyze the data. RESULTS: Nucleoli were identified in 61% of cases, whereas nuclei were recognized in 100% of cases (p<0.005). Nucleoli presence in all four segments in the same temporal bone (TB) was observed in 69 cases (92%), whereas nuclei were identified in all four segments in 103 cases (83.7%) (p<0.001). The junior investigators requested a double check by the seniors in 25 (20.3%) cases for identifying and counting nucleoli, whereas the senior researchers showed no doubts in their identification and count. The only parameter positively affecting nucleoli identification in tissue preparation was bone removal for <12 h with respect to longer post-mortem time (p<0.001). CONCLUSION: We suggest counting nuclei, rather than nucleoli, for spiral ganglion computation because of easier recognition of nuclei, especially in case of investigator's limited experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".