Brief communication: Test of a method to identify double‐zonal osteon in polarized light microscopy
Bibliographic record
Abstract
OBJECTIVES: Double-zonal osteons (DZ) have been of interest in paleopathological research because they might be linked to physiological pathology. DZ are thought to be evidence of arrested osteon formation with a brief but abrupt increase in mineralization of lamellae occurring during bone remodeling. Originally identified from microradiographs as hypermineralized rings, recent studies have identified DZ from linear polarized light microscopy (PLM). However, PLM does not guarantee the adequate detection of DZ since PLM captures bone birefringence and not hyper-mineralization. Scanning electron microscopy with backscatter electrons (BSE-SEM) allows observation of DZ by detecting differences in mineralization. The purpose of this study is to investigate whether DZ, as identified by BSE-SEM, can indeed be identified with PLM. MATERIALS AND METHODS: The sample consists of an archaeological collection of adult midshaft femurs (n = 30) from St. Matthew cemetery, Quebec City (1771-1860). DZ were identified and counted independently with PLM and BSE-SEM for the same sections. Results from both methods were compared. RESULTS: Chi-square test shows that there was no significant difference between the two methods (p = 0.404). No significant bias was found on Bland-Altman analysis and Cohen's kappa shows a substantial agreement between the two methods (Κ = 0.66). PLM shows a good accuracy (sensitivity 79%, specificity 99.4%) and reliability (Positive Predictive Value: 86.71%; Negative Predictive Value: 99.45%). DISCUSSION: These findings indicate that the two methods are interchangeable. PLM, using our proposed protocol, is reliable to accurately identify DZ. We discuss how PLM and BSE-SEM that measure different features of the bone tissue can converge on the identification of DZ.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".