Bibliographic record
Abstract
I take a lot of ribbing about how I eat, sleep, and breathe my “quality sermon” to anyone who will listen. And the critics are right in that I eat restaurant food, sleep in hotel beds, and breathe dry stale airplane and meeting room air to deliver my message. You know, having a passion for one’s profession is critical to one’s happiness and success; therefore, I apologize neither for my passion nor for my sermon. In the plenary session, “Simply Powerful QMS,” at the CLSI meeting this past April in Philadelphia, Dr. Michael Noble, from the University of British Columbia in Vancouver, BC, showed a visual that summarized the part of my quality sermon where I make a plea for laboratories to just get out there and get started on the road to quality management. He calls it the “Quality Management Ladder to Success.” The visual depicts an upright ladder with “Awareness” at the ground level and “Achievement” above the top rung. The middle rungs are labeled with the increasing states of quality management system implementation, described as follows (used with permission): Quality management is not quality control nor is it simply a new name for quality assurance. Quality management is a systematized approach for managing the quality of any organization’s processes, products, or services. As experienced laboratory professionals, we know about quality control and understand the role of quality assurance in laboratory activities. However, quality management has a significantly bigger body of knowledge that one must start to absorb. Therefore, the first rung of the ladder is learning about quality management systems and how they apply in your laboratory. Once one has learned about the elements of quality management—which include document control, management of nonconforming events, and process validation, among others— one must start doing these activities in the laboratory. Only the latest approved versions of all documents are readily available to staff where they work. Every unexpected event and complaint is documented and investigated because these events reveal problematic laboratory processes. And every new or changed process—whether or not it includes testing—is validated as performing as intended before being used in the live environment. On this next rung up the ladder, your laboratory has to do these things as part of quality management. Quality management processes and procedures need to be integrated into the staff’s work routines so that they are performed as part of doing laboratory work. For example, assignments are made for the equipment maintenance schedule and staff is expected to perform their assigned maintenance duties according to the schedule by fitting them into their daily work—no excuses. In another example, every person is responsible for communicating that it’s time to reorder a supply or reagent because the minimum stock level has been reached—and not wait for “someone else” to do it. Integrating quality management activities into the responsibilities of every staff person is the next rung up the ladder. Quality management tools and techniques used in business and industry are applicable in the laboratory environment. Consider the impact that the Toyota Lean Production Process is having on laboratory sample receiving and processing and on the movement of surgically-removed tissues through the histopathology and pathologist examination processes. Have you ever thought of using a control chart to track the daily temperatures of your laboratory’s temperature-controlled equipment so that there is one uniform tool used across the entire laboratory? What about process flowcharts, FMEA, and cause-and-effect diagrams? The next rung up the ladder is to use quality management tools and techniques at every available opportunity in laboratory work. When people understand and do their respective work processes and procedures, integrate quality activities into their personal work routines, and use tools to continuously measure and monitor performance, only then can it be said that the laboratory is living in quality. This is proved by providing objective evidence that everything that needs to be done to meet requirements was done, on time, with acceptable results or followup as necessary. Only then will your laboratory have hit the top rung and achieved living in quality. Critics or not, I’ll continue to eat, sleep, breathe, and live this quality management message so that your laboratory can make its best contribution to the patients we serve. This Month’s Quality Quote: “There is a ladder. The ladder is always there, hanging innocently, close to the side of the wreck.” —Adrienne Rich, Diving into the Wreck
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".