Evolution of anatomic pathology workload from 2011 to 2019 assessed in a regional hospital laboratory via 574,093 pathology reports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Quantify changes in workload in relation to the anatomic pathologist workforce. METHODS: In house pathology reports for cytology and surgical specimens from a regional hospital laboratory over a nine- year period (2011-2019) were analyzed, using custom computer code. Report length for the diagnosis+microscopic+synoptic report, number of blocks, billing classification (L86x codes), billings, national workload model (L4E 2018), regional workload model (W2Q), case count, and pathologist workforce in full-time equivalents (FTEs) were quantified. Randomly selected cases (n = 1,100) were audited to assess accuracy. RESULTS: The study period had 574,093 pathology reports that could be analyzed. The coding accuracy was estimated at 95%. From 2011 to 2019: cases/year decreased 6% (66,056 to 61,962), blocks/year increased 20% (236,197 to 283,751), L4E workload units increased 23% (165,276 to 203,894), W2Q workload units increased 21% (149,841 to 181,321), report lines increased 19% (606,862 to 723,175), workforce increased 1% (30.42 to 30.77 FTEs), billings increased 13% ($6,766,927 to $7,677,109). W2Q in relation to L4E underweights work in practices with large specimens by up to a factor of 2x. CONCLUSIONS: Work by L4E for large specimens is underrated by W2Q. Reporting requirements and pathology work-up have increased workload per pathology case. Work overall has increased significantly without a commensurate workforce increase. The significant practice changes in the pathology work environment should prompt local investment in the anatomic pathology workforce.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it